diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index f4b38798..b73f7e62 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,23 @@
Change Log
==========
+v22.11.17
+--------
+
+Main changes:
+
+- Refactor :code:`Distortions()` to a list or simple-format dict of :code:`Defect` objects as input.
+ Same for :code:`Distortions.from_structures()`
+- Update defect naming to :code:`{Defect.name}_s{Defect.defect_site_index}` for vacancies/substitutions and
+ :code:`{Defect.name}_m{Defect.multiplicity}` for interstitials. Append "a", "b", "c" etc in cases of inequivalent
+ defects
+- Make :code:`ShakeNBreak` compatible with most recent :code:`pymatgen` and :code:`pymatgen-analysis-defects` packages.
+- Update legend format in plots and site index/multiplicity labelling, make default format png.
+- Update default charge state setting to match :code:`pymatgen-analysis-defects` oxi state + padding approach.
+- A lot of additional warning and error catches.
+- Miscellaneous warnings and docs updates.
+
+
v22.11.7
--------
diff --git a/README.md b/README.md
index bbe029c1..b005d4d3 100644
--- a/README.md
+++ b/README.md
@@ -35,6 +35,8 @@ If using `VASP`, in order for `ShakeNBreak` to automatically generate the pseudo
```
Within your `VASP` pseudopotential top directory, you should have a folder named `POT_GGA_PAW_PBE` which contains the `POTCAR.X(.gz)` files (in this case for PBE `POTCAR`s). More details given [here](https://pymatgen.org/installation.html#potcar-setup).
+The font Montserrat ([Open Font License](https://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=OFL)) will be installed with the package, and will be used by default for plotting.
+
### Developer installation
For development work, ShakeNBreak can also be installed from a copy of the source directory:
@@ -77,12 +79,19 @@ More information about each function and its inputs/outputs are available from t
We recommend at least looking through the [tutorials](https://shakenbreak.readthedocs.io/en/latest/Tutorials.html) when first starting to use `ShakeNBreak`, to familiarise yourself with the full functionality and workflow.
## Code Compatibility
-`ShakeNBreak` is built to natively function using `pymatgen` `Defect` objects ([docs available here](https://materialsproject.github.io/pymatgen-analysis-defects/)) and be compatible with the most recent version of `pymatgen`. If you are receiving `pymatgen`-related errors when using `ShakeNBreak`, you may need to update `pymatgen` and/or `ShakeNBreak`, which can be done with:
+`ShakeNBreak` is built to natively function using `pymatgen` `Defect` objects ([docs available here](https://materialsproject.github.io/pymatgen-analysis-defects/)) and be compatible with the most recent version of `pymatgen`.
+If you are receiving `pymatgen`-related errors when using `ShakeNBreak`, you may need to update `pymatgen` and/or `ShakeNBreak`, which can be done with:
```bash
pip install --upgrade pymatgen shakenbreak
```
-`ShakeNBreak` can take `pymatgen` `Defect` objects as input (to then generate the trial distorted structures), **_but also_** can take in `pymatgen` `Structure` objects, `doped` defect dictionaries or structure files (e.g. `POSCAR`s for `VASP`) as inputs. As such, it should be compatible with any defect code (such as `doped`, `DASP`, `PyLada`, `PyCDT`, `Spinney`, `DefAP`, `PyDEF`, `pydefect`...) that generates these files.
+`ShakeNBreak` can take `pymatgen` `Defect` objects as input (to then generate the trial distorted structures),
+**_but also_** can take in `pymatgen` `Structure` objects, `doped` defect dictionaries or structure files
+(e.g. `POSCAR`s for `VASP`) as inputs. As such, it should be compatible with any defect code
+(such as [`doped`](https://github.com/SMTG-UCL/doped), [`pydefect`](https://github.com/kumagai-group/pydefect),
+[`PyCDT`](https://github.com/mbkumar/pycdt), [`PyLada`](https://github.com/pylada/pylada-defects),
+[`DASP`](http://hzwtech.com/files/software/DASP/htmlEnglish/index.html), [`Spinney`](https://gitlab.com/Marrigoni/spinney/-/tree/master),
+[`DefAP`](https://github.com/DefAP/defap), [`PyDEF`](https://github.com/PyDEF2/PyDEF-2.0)...) that generates these files.
Please let us know if you have any issues with compatibility, or if you would like to see any additional features added to `ShakeNBreak` to make it more compatible with your code.
## Contributing
diff --git a/docs/Analysis.rst b/docs/Analysis.rst
index 0a3127a8..7ceabdc3 100644
--- a/docs/Analysis.rst
+++ b/docs/Analysis.rst
@@ -18,7 +18,7 @@ Alternatively, we can run from a different directory and specify the defect to p
.. code:: bash
- $ snb-parse --defect vac_1_Cd_0 --path defects_folder --code FHI-aims
+ $ snb-parse --defect v_Cd_s0_0 --path defects_folder --code FHI-aims
Where ``defects_folder`` is the path to the top level directory containing the defect folder, and is only required if
different from the current directory.
@@ -32,7 +32,7 @@ in a given/current directory using the ``-a``/``--all`` flag:
This generates a ``yaml`` file for each defect, mapping each distortion to the
final energy of the relaxed structures (in eV). These files are saved to the
-corresponding defect directory (e.g. ``defects_folder/vac_1_Cd_0/vac_1_Cd_0.yaml``).
+corresponding defect directory (e.g. ``defects_folder/v_Cd_s0_0/v_Cd_s0_0.yaml``).
.. code:: yaml
@@ -61,7 +61,7 @@ was used (if not :code:`VASP`) and which reference structure to use (default = `
.. code:: bash
- $ snb-analyse --defect vac_1_Cd_0 --code FHI-aims --path defects_folder --ref_struct -0.4 --verbose
+ $ snb-analyse --defect v_Cd_s0_0 --code FHI-aims --path defects_folder --ref_struct -0.4 --verbose
Again if we want to analyse the results for **all** defects present in a given/current directory, we can use the
``-a``/``--all`` flag:
@@ -90,7 +90,7 @@ the defect directory:
which will generate a figure like the one below:
-.. image:: ./vac_1_Cd_0.svg
+.. image:: ./v_Cd_s0_0.svg
:width: 400px
..
@@ -103,7 +103,7 @@ structures, using the ``-cb``/``--colorbar`` flag:
$ snb-plot -cb
-.. image:: ./vac_1_Cd_0_colorbar.svg
+.. image:: ./v_Cd_s0_0_colorbar.svg
:width: 450px
..
@@ -114,7 +114,7 @@ was used (if not :code:`VASP`) and other options (what ``metric`` to use for col
.. code:: bash
- $ snb-plot --defect vac_1_Cd_0 --code FHI-aims --path defects_folder --colorbar -0.4 --metric disp --units meV --verbose
+ $ snb-plot --defect v_Cd_s0_0 --code FHI-aims --path defects_folder --colorbar -0.4 --metric disp --units meV --verbose
Again if we want to plot the results for **all** defects present in a given/current directory, we can use the
``-a``/``--all`` flag:
@@ -143,7 +143,7 @@ For example, if we have the following directory structure
.. code:: bash
./
- |--- vac_1_Cd_0/ <-- Neutral Cd vacancy
+ |--- v_Cd_s0_0/ <-- Neutral Cd vacancy
| |--- Unperturbed
| |
| |--- Bond_Distortion_-30.0% <-- Favourable distortion
@@ -151,7 +151,7 @@ For example, if we have the following directory structure
| |--- Bond_Distortion_30.0%
| | ...
|
- |--- vac_1_Cd_-1/ <-- Negatively charged Cd vacancy
+ |--- v_Cd_s0_-1/ <-- Negatively charged Cd vacancy
|--- Unperturbed
| ...
|--- Bond_Distortion_50% <-- Favourable distortion
@@ -171,7 +171,7 @@ for the code specified with the flag ``--code`` (default = :code:`VASP`).
.. code:: bash
./
- |--- vac_1_Cd_0/
+ |--- v_Cd_s0_0/
| |--- Unperturbed
| |
| |--- Bond_Distortion_-30.0% <-- Favourable distortion
@@ -180,7 +180,7 @@ for the code specified with the flag ``--code`` (default = :code:`VASP`).
| | ...
| |--- Bond_Distortion_50.0%_from_-1 <-- Distortion from the -1 charge state
|
- |--- vac_1_Cd_-1/
+ |--- v_Cd_s0_-1/
|--- Unperturbed
| ...
|--- Bond_Distortion_50% <-- Favourable distortion
@@ -214,7 +214,7 @@ This command will generate a ``Groundstate`` directory within each defect folder
.. code:: bash
./
- |--- vac_1_Cd_0/
+ |--- v_Cd_s0_0/
| |--- Unperturbed
| |
| |--- Bond_Distortion_-30.0%
@@ -224,7 +224,7 @@ This command will generate a ``Groundstate`` directory within each defect folder
| |--- Groundstate
| |--- POSCAR <-- Ground state structure
|
- |--- vac_1_Cd_-1/
+ |--- v_Cd_s0_-1/
|--- Unperturbed
| ...
|--- Bond_Distortion_50%
diff --git a/docs/Code_Compatibility.rst b/docs/Code_Compatibility.rst
index e9bb6b38..d8756919 100644
--- a/docs/Code_Compatibility.rst
+++ b/docs/Code_Compatibility.rst
@@ -1,12 +1,23 @@
Code Compatibility
========================
-:code:`ShakeNBreak` is built to natively function using :code:`pymatgen` :code:`Defect` objects (`docs available here `_) and be compatible with the most recent version of :code:`pymatgen`. If you are receiving :code:`pymatgen`-related errors when using :code:`ShakeNBreak`, you may need to update :code:`pymatgen` and/or :code:`ShakeNBreak`, which can be done with:
+:code:`ShakeNBreak` is built to natively function using :code:`pymatgen` :code:`Defect` objects
+(`docs available here `_) and be compatible with the
+most recent version of :code:`pymatgen`. If you are receiving :code:`pymatgen`-related errors when using
+:code:`ShakeNBreak`, you may need to update :code:`pymatgen` and/or :code:`ShakeNBreak`, which can be done with:
.. code:: bash
pip install --upgrade pymatgen shakenbreak
-:code:`ShakeNBreak` can take :code:`pymatgen` :code:`Defect` objects as input (to then generate the trial distorted structures), **but also** can take in :code:`pymatgen` :code:`Structure` objects, :code:`doped` defect dictionaries or structure files (e.g. :code:`POSCAR`\s for :code:`VASP`) as inputs. As such, it should be compatible with any defect code (such as :code:`doped`, :code:`DASP`, :code:`PyLada`, :code:`PyCDT`, :code:`Spinney`, :code:`DefAP`, :code:`PyDEF`, :code:`pydefect`...) that generates these files.
-Please let us know if you have any issues with compatibility, or if you would like to see any additional features added to :code:`ShakeNBreak` to make it more compatible with your code.
+:code:`ShakeNBreak` can take :code:`pymatgen` :code:`Defect` objects as input (to then generate the trial distorted
+structures), **but also** can take in :code:`pymatgen` :code:`Structure` objects, :code:`doped` defect dictionaries or
+structure files (e.g. :code:`POSCAR`\s for :code:`VASP`) as inputs. As such, it should be compatible with any defect code
+(such as `doped `_, `pydefect `_,
+`PyCDT `_, `PyLada `_,
+`DASP `_, `Spinney `_,
+`DefAP `_, `PyDEF `_...) that generates these files.
+
+Please let us know if you have any issues with compatibility, or if you would like to see any additional features added
+to :code:`ShakeNBreak` to make it more compatible with your code.
diff --git a/docs/Installation.rst b/docs/Installation.rst
index 714137d5..cd6628b3 100644
--- a/docs/Installation.rst
+++ b/docs/Installation.rst
@@ -19,6 +19,12 @@ Within your ``VASP`` pseudopotential top directory, you should have a folder nam
which contains the ``POTCAR.X(.gz)`` files (in this case for PBE ``POTCARs``). More details given
`here `_.
+.. NOTE::
+ The font `Montserrat `
+ (`Open Font License `_)
+ will be installed with the package, and will be used by default for plotting. If you prefer to use a different
+ font, you can change the font in the ``matplotlib`` style sheet (in ``shakenbreak/shakenbreak.mplstyle``).
+
Developer's installation (*optional*)
-----------------------------------------
diff --git a/docs/ShakeNBreak_Example_Workflow.ipynb b/docs/ShakeNBreak_Example_Workflow.ipynb
index 68a48691..c7d8a2f1 100644
--- a/docs/ShakeNBreak_Example_Workflow.ipynb
+++ b/docs/ShakeNBreak_Example_Workflow.ipynb
@@ -70,10 +70,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Pymatgen version: 2022.10.22\n",
+ "Pymatgen version: 2022.11.7\n",
"Pymatgen-analysis-defects version: 2022.10.28\n",
"Ase version: 3.22.1\n",
- "ShakeNBreak version: 22.10.14\n"
+ "ShakeNBreak version: 22.11.7\n"
]
}
],
@@ -139,8 +139,8 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "9e943689-3bad-41d6-b52b-f4b26453715e",
+ "execution_count": 2,
+ "id": "c6388277-3097-4b45-9abd-e84217f489b0",
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -157,28 +157,23 @@
"v_Cd = Vacancy(\n",
" structure=bulk_supercell,\n",
" site=bulk_supercell[0], # First Cd site\n",
- " user_charges=[0], # Defect charge states\n",
- ")\n",
- "\n",
- "# Store defects in a dictionary\n",
- "V_Cd_dict = {\n",
- " \"vacancies\": [v_Cd,]\n",
- "}"
+ " user_charges=[-2, -1, 0], # Defect charge states\n",
+ ")"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "dcf155cb",
+ "execution_count": 3,
+ "id": "708c60df-0f16-4c5b-98ac-a1004f3d7439",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[0]"
+ "[-2, -1, 0]"
]
},
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -190,8 +185,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "id": "2867d06a",
+ "execution_count": 4,
+ "id": "8def7b30-92d9-4b59-8554-489b6f262381",
"metadata": {},
"outputs": [
{
@@ -210,8 +205,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "4e21b8c3",
+ "execution_count": 5,
+ "id": "82ebb90d-fc12-4201-8318-de122b9dea5e",
"metadata": {},
"outputs": [
{
@@ -230,15 +225,25 @@
},
{
"cell_type": "markdown",
- "id": "c035d70a",
+ "id": "e2c5b765-29b7-4e53-b365-98ea17dfce41",
"metadata": {},
+ "source": [
+ "**Alternatively,** if you have already generated your defect structure files with a different defects code, these can be directly fed to `ShakeNBreak` with the `Distortions.from_structures()` method as shown below."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12761153-e1a1-495b-93b4-354d2846ca92",
+ "metadata": {
+ "tags": []
+ },
"source": [
"### *Optional*: Generate defects with `doped`/`PyCDT` (instead of `Pymatgen`)"
]
},
{
"cell_type": "markdown",
- "id": "653906e1",
+ "id": "f99dbe67-bf82-4c06-9cb6-886b2db763da",
"metadata": {},
"source": [
"If you prefer to use `Doped`/`PyCDT` to generate defects, you should do it in a different `Python` environment to the `ShakeNBreak` one, as currently (18/10/22) they require different `pymatgen` versions. \n",
@@ -249,7 +254,7 @@
},
{
"cell_type": "raw",
- "id": "c5190f31",
+ "id": "3dfdd778-1989-457f-b462-3bdf0db3b385",
"metadata": {},
"source": [
"# To generate the CdTe vacancies with doped, we can use the lines below.\n",
@@ -283,8 +288,8 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "729796ff",
+ "execution_count": 6,
+ "id": "7ca7752a-af2f-4e21-ae5b-7eeba9dadd7e",
"metadata": {},
"outputs": [],
"source": [
@@ -299,8 +304,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "89272bdf",
+ "execution_count": 7,
+ "id": "4247b8b4-b604-41f6-ba28-2dda804377a1",
"metadata": {},
"outputs": [
{
@@ -321,135 +326,6 @@
"print(\"Keys of bulk entry:\", doped_V_Cd_dict[\"bulk\"].keys())"
]
},
- {
- "cell_type": "markdown",
- "id": "68bf9b71",
- "metadata": {},
- "source": [
- "Additionally, defects can also be generated from a dictionary of structures using `Distortions.from_structures()`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d0a57292",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[0;31mSignature:\u001b[0m\n",
- "\u001b[0mDistortions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_structures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mstructures_dict\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0moxidation_states\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdict_number_electrons_user\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdistortion_increment\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mbond_distortions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mlocal_rattle\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mstdev\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdistorted_elements\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mDocstring:\u001b[0m\n",
- "Initialise Distortion() class using a dictionary of bulk and defect\n",
- "structures (instead of pymatgen-analysis-defects Defect() objects).\n",
- "\n",
- "Args:\n",
- " structures_dict (:obj:`dict`):\n",
- " Dictionary of defect and bulk structures\n",
- " (eg.:\n",
- " {\n",
- " \"vacancies\": [Structure, Structure, ...],\n",
- " \"substitutions\": [Structure, ...],\n",
- " \"interstitials\": [Structure, ...],\n",
- " \"bulk\": Structure,\n",
- " })\n",
- " Alternatively, the defect index or the defect fractional\n",
- " coordinates can provided for each defect like this:\n",
- " {\n",
- " \"vacancies\": [\n",
- " {\"structure\": Structure, \"defect_coords\": [0.5, 0.5, 0.5]},\n",
- " ...\n",
- " ],\n",
- " \"interstitials\": [\n",
- " {\"structure\": Structure, \"defect_index\": -1},\n",
- " ...\n",
- " ],\n",
- " \"bulk\": Structure,\n",
- " }\n",
- " oxidation_states (:obj:`dict`):\n",
- " Dictionary of oxidation states for species in your material,\n",
- " used to determine the number of defect neighbours to distort\n",
- " (e.g {\"Cd\": +2, \"Te\": -2}). If none is provided, the oxidation\n",
- " states will be guessed based on the bulk composition and most\n",
- " common oxidation states of any extrinsic species.\n",
- " dict_number_electrons_user (:obj:`dict`):\n",
- " Optional argument to set the number of extra/missing charge\n",
- " (negative of electron count change) for the input defects\n",
- " in their neutral state, as a dictionary with format\n",
- " {'defect_name': charge_change} where charge_change is the\n",
- " negative of the number of extra/missing electrons.\n",
- " (Default: None)\n",
- " distortion_increment (:obj:`float`):\n",
- " Bond distortion increment. Distortion factors will range from\n",
- " 0 to +/-0.6, in increments of `distortion_increment`.\n",
- " Recommended values: 0.1-0.3\n",
- " (Default: 0.1)\n",
- " bond_distortions (:obj:`list`):\n",
- " List of bond distortions to apply to nearest neighbours,\n",
- " instead of the default set (e.g. [-0.5, 0.5]).\n",
- " (Default: None)\n",
- " local_rattle (:obj:`bool`):\n",
- " Whether to apply random displacements that tail off as we move\n",
- " away from the defect site. Not recommended as typically worsens\n",
- " performance. If False (default), all supercell sites are rattled\n",
- " with the same amplitude (full rattle).\n",
- " (Default: False)\n",
- " stdev (:obj:`float`):\n",
- " Standard deviation (in Angstroms) of the Gaussian distribution\n",
- " from which random atomic displacement distances are drawn during\n",
- " rattling. Recommended values: 0.25, or 0.15 for strongly-bound\n",
- " /ionic materials.\n",
- " (Default: 0.25)\n",
- " distorted_elements (:obj:`dict`):\n",
- " Optional argument to specify the neighbouring elements to\n",
- " distort for each defect, in the form of a dictionary with\n",
- " format {'defect_name': ['element1', 'element2', ...]}\n",
- " (e.g {'vac_1_Cd': ['Te']}). If None, the closest neighbours to\n",
- " the defect are chosen.\n",
- " (Default: None)\n",
- " **kwargs:\n",
- " Additional keyword arguments to pass to `hiphive`'s\n",
- " `mc_rattle` function. These include:\n",
- " - d_min (:obj:`float`):\n",
- " Minimum interatomic distance (in Angstroms). Monte Carlo rattle\n",
- " moves that put atoms at distances less than this will be heavily\n",
- " penalised.\n",
- " (Default: 2.25)\n",
- " - max_disp (:obj:`float`):\n",
- " Maximum atomic displacement (in Angstroms) during Monte Carlo\n",
- " rattling. Rarely occurs and is used primarily as a safety net.\n",
- " (Default: 2.0)\n",
- " - max_attempts (:obj:`int`):\n",
- " Limit for how many attempted rattle moves are allowed a single atom.\n",
- " - active_atoms (:obj:`list`):\n",
- " List of the atomic indices which should undergo Monte\n",
- " Carlo rattling. By default, all atoms are rattled.\n",
- " (Default: None)\n",
- " - seed (:obj:`int`):\n",
- " Seed for setting up NumPy random state from which random\n",
- " numbers are generated.\n",
- "\u001b[0;31mFile:\u001b[0m ~/Python_Modules/shakenbreak/shakenbreak/input.py\n",
- "\u001b[0;31mType:\u001b[0m method\n"
- ]
- }
- ],
- "source": [
- "from shakenbreak.input import Distortions\n",
- "Distortions.from_structures?"
- ]
- },
{
"cell_type": "markdown",
"id": "8c639b14-b7d4-415b-8c55-9e2a1a188923",
@@ -509,8 +385,8 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "3ff9be61-6cf8-48c0-bd35-6e8775b70158",
+ "execution_count": 6,
+ "id": "b1149e22-0eb6-4ccf-8712-410b1f826a5c",
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -524,8 +400,8 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "40013965-8d9f-4617-bc76-3ad8cd409e00",
+ "execution_count": 7,
+ "id": "5ab3eb32-60d0-43fb-8cd4-8390d6fa184d",
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -547,48 +423,159 @@
"# If not specified, the code will guess these, otherwise you can specify as such:\n",
"# oxidation_states = {\"Cd\": +2, \"Te\": -2} # specify atom oxidation states\n",
"\n",
- "# Create an instance of Distortion class with the defect dictionary and the distortion parameters\n",
+ "# Create an instance of Distortion class with the defects and distortion parameters\n",
"# If distortion parameters are not specified, the default values are used\n",
- "Dist = Distortions(\n",
- " defects_dict=dict(V_Cd_dict),\n",
- " #oxidation_states=oxidation_states, # explicitly specify oxidation states\n",
- ")"
+ "Dist = Distortions(defects=v_Cd)"
]
},
{
- "cell_type": "code",
- "execution_count": 13,
- "id": "3be9bc57",
+ "cell_type": "markdown",
+ "id": "b20b4b3a-24d3-4489-b475-5ba44e2f077d",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Oxidation states were not explicitly set, thus have been guessed as {'Cd': 2.0, 'Te': -2.0}. If this is unreasonable you should manually set oxidation_states\n"
- ]
- }
- ],
"source": [
- "# Alternatively, if you used Doped/PyCDT for defect generation, you can use the `from_dict` method:\n",
+ "The `Distortions()` class is flexible to the user input, so can take single `pymatgen` `Defect` objects, a list of `Defect`s, or a dictionary of `Defect`s (in which case the dictionary keys are used as the defect names) as inputs.\n",
"\n",
- "Dist = Distortions.from_dict(\n",
- " doped_defects_dict=doped_V_Cd_dict,\n",
- ")"
+ "The defect dictionary output by `ChargedDefectStructures` in `doped`/`PyCDT` can also be used to initialise `Distortions`, with the code: \n",
+ "```python\n",
+ "Dist = Distortions(defects=doped_V_Cd_dict)\n",
+ "```\n",
+ "\n",
+ "These possibilities as well as the optional distortion parameters are detailed in the `Distortions` class docstring:"
]
},
{
- "cell_type": "markdown",
- "id": "9857099f",
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "89089e1c-e3aa-4457-9943-76ea59481db6",
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\u001B[0;31mInit signature:\u001B[0m\n",
+ "\u001B[0mDistortions\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mdefects\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mUnion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mlist\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdict\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0moxidation_states\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mUnion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mdict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mNoneType\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mpadding\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mint\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mdict_number_electrons_user\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mUnion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mdict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mNoneType\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mdistortion_increment\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mfloat\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;36m0.1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mbond_distortions\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mUnion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mlist\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mNoneType\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mlocal_rattle\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mbool\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mFalse\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0mdistorted_elements\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mUnion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mdict\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mNoneType\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mmc_rattle_kwargs\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\n",
+ "\u001B[0;34m\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+ "\u001B[0;31mDocstring:\u001B[0m \n",
+ "Class to apply rattle and bond distortion to all defects in `defects`\n",
+ "(each defect as a pymatgen.analysis.defects.core.Defect() object).\n",
+ "\u001B[0;31mInit docstring:\u001B[0m\n",
+ "Args:\n",
+ " defects (:obj:`dict_or_list_or_Defect`):\n",
+ " List or dictionary of pymatgen.analysis.defects.core.Defect() objects.\n",
+ " E.g.: [Vacancy(), Interstitial(), Substitution(), ...], or single Defect().\n",
+ " In this case, generated defect folders will be named in the format:\n",
+ " \"{Defect.name}_m{Defect.multiplicity}\" for interstitials and\n",
+ " \"{Defect.name}_s{Defect.defect_site_index}\" for vacancies and substitutions.\n",
+ " The labels \"a\", \"b\", \"c\"... will be appended for defects with multiple\n",
+ " inequivalent sites.\n",
+ "\n",
+ " Alternatively, if specific defect folder names are desired, `defects` can\n",
+ " be input as a dictionary in the format {\"defect name\": Defect()}.\n",
+ " E.g.: {\"vac_name\": Vacancy(), \"vac_2_name\": Vacancy(), ...,\n",
+ " \"int_name\": Interstitial(), \"sub_name\": Substitution(), ...}.\n",
+ "\n",
+ " Defect charge states (from which bond distortions are determined) are\n",
+ " taken from the `Defect.user_charges` property. If this is not set,\n",
+ " charge states are set to the range: 0 – {Defect oxidation state}\n",
+ " with a `padding` (default = 1) on either side of this range.\n",
+ "\n",
+ " Alternatively, a defects dict generated by `ChargedDefectStructures`\n",
+ " from `doped`/`PyCDT` can also be used as input, and the defect names\n",
+ " and charge states generated by these codes will be used\n",
+ " E.g.: {\"bulk\": {..}, \"vacancies\": [{...}, {...},], ...}\n",
+ " oxidation_states (:obj:`dict`):\n",
+ " Dictionary of oxidation states for species in your material,\n",
+ " used to determine the number of defect neighbours to distort\n",
+ " (e.g {\"Cd\": +2, \"Te\": -2}). If none is provided, the oxidation\n",
+ " states will be guessed based on the bulk composition and most\n",
+ " common oxidation states of any extrinsic species.\n",
+ " padding (:obj:`int`):\n",
+ " If `Defect.user_charges` is not set, charge states are set to\n",
+ " the range: 0 – {Defect oxidation state}, with a `padding`\n",
+ " (default = 1) on either side of this range.\n",
+ " dict_number_electrons_user (:obj:`dict`):\n",
+ " Optional argument to set the number of extra/missing charge\n",
+ " (negative of electron count change) for the input defects\n",
+ " in their neutral state, as a dictionary with format\n",
+ " {'defect_name': charge_change} where charge_change is the\n",
+ " negative of the number of extra/missing electrons.\n",
+ " (Default: None)\n",
+ " distortion_increment (:obj:`float`):\n",
+ " Bond distortion increment. Distortion factors will range from\n",
+ " 0 to +/-0.6, in increments of `distortion_increment`.\n",
+ " Recommended values: 0.1-0.3\n",
+ " (Default: 0.1)\n",
+ " bond_distortions (:obj:`list`):\n",
+ " List of bond distortions to apply to nearest neighbours,\n",
+ " instead of the default set (e.g. [-0.5, 0.5]).\n",
+ " (Default: None)\n",
+ " local_rattle (:obj:`bool`):\n",
+ " Whether to apply random displacements that tail off as we move\n",
+ " away from the defect site. Not recommended as typically worsens\n",
+ " performance. If False (default), all supercell sites are rattled\n",
+ " with the same amplitude (full rattle).\n",
+ " (Default: False)\n",
+ " distorted_elements (:obj:`dict`):\n",
+ " Optional argument to specify the neighbouring elements to\n",
+ " distort for each defect, in the form of a dictionary with\n",
+ " format {'defect_name': ['element1', 'element2', ...]}\n",
+ " (e.g {'vac_1_Cd': ['Te']}). If None, the closest neighbours to\n",
+ " the defect are chosen.\n",
+ " (Default: None)\n",
+ " **mc_rattle_kwargs:\n",
+ " Additional keyword arguments to pass to `hiphive`'s\n",
+ " `mc_rattle` function. These include:\n",
+ " - stdev (:obj:`float`):\n",
+ " Standard deviation (in Angstroms) of the Gaussian distribution\n",
+ " from which random atomic displacement distances are drawn during\n",
+ " rattling. Default is set to 10% of the nearest neighbour distance\n",
+ " in the bulk supercell.\n",
+ " - d_min (:obj:`float`):\n",
+ " Minimum interatomic distance (in Angstroms) in the rattled\n",
+ " structure. Monte Carlo rattle moves that put atoms at distances\n",
+ " less than this will be heavily penalised. Default is to set this\n",
+ " to 80% of the nearest neighbour distance in the bulk supercell.\n",
+ " - max_disp (:obj:`float`):\n",
+ " Maximum atomic displacement (in Angstroms) during Monte Carlo\n",
+ " rattling. Rarely occurs and is used primarily as a safety net.\n",
+ " (Default: 2.0)\n",
+ " - max_attempts (:obj:`int`):\n",
+ " Limit for how many attempted rattle moves are allowed a single atom.\n",
+ " - active_atoms (:obj:`list`):\n",
+ " List of the atomic indices which should undergo Monte\n",
+ " Carlo rattling. By default, all atoms are rattled.\n",
+ " (Default: None)\n",
+ " - seed (:obj:`int`):\n",
+ " Seed from which rattle random displacements are generated. Default\n",
+ " is to set seed = int(distortion_factor*100) (i.e. +40% distortion ->\n",
+ " distortion_factor = 1.4 -> seed = 140, Rattled ->\n",
+ " distortion_factor = 1 (no bond distortion) -> seed = 100)\n",
+ "\u001B[0;31mFile:\u001B[0m ~/Library/CloudStorage/OneDrive-ImperialCollegeLondon/Bread/Projects/Packages/ShakeNBreak/shakenbreak/input.py\n",
+ "\u001B[0;31mType:\u001B[0m type\n",
+ "\u001B[0;31mSubclasses:\u001B[0m \n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "We can check the distortion parameters using some of the class properties:"
+ "Distortions?"
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "78543a71",
+ "execution_count": 9,
+ "id": "a7e5038b-eec0-48ab-9107-a2fe5ff0d9a1",
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -612,179 +599,74 @@
},
{
"cell_type": "markdown",
- "id": "2c5605eb",
+ "id": "57164286-6e2e-48e8-90dd-1c5f39784716",
"metadata": {},
"source": [
- "```{tip}\n",
- "You can restrict the ions that are distorted to a certain element using the keyword `distorted_elements`. \n",
- "We can check it using the class attribute:\n",
- "```"
+ "As mentioned above, we can also initialise `Distortions` directly from our pre-generated defect structures, using the `Distortions.from_structures()` method like this:"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "52f9e11b",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
+ "execution_count": 10,
+ "id": "2e3ae070-d915-45ca-a2e6-428ac1759979",
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "User defined elements to distort: None\n"
+ "Defect charge states will be set to the range: 0 – {Defect oxidation state}, with a `padding = 1` on either side of this range.\n",
+ "Oxidation states were not explicitly set, thus have been guessed as {'Cd': 2.0, 'Te': -2.0}. If this is unreasonable you should manually set oxidation_states\n"
]
}
],
"source": [
- "print(\"User defined elements to distort:\", Dist.distorted_elements)"
+ "from pymatgen.core.structure import Structure\n",
+ "V_Cd_struc = Structure.from_file(\"../tests/data/vasp/CdTe/CdTe_V_Cd_POSCAR\")\n",
+ "bulk_struc = Structure.from_file(\"../tests/data/vasp/CdTe/CdTe_Bulk_Supercell_POSCAR\")\n",
+ "Dist = Distortions.from_structures(defects = V_Cd_struc, bulk = bulk_struc)"
]
},
{
"cell_type": "markdown",
- "id": "5c50bb0b",
+ "id": "2c5605eb",
"metadata": {},
"source": [
- "If `None`, it means no restrictions, so nearest neighbours are distorted (recommended default, \n",
- "unless you have reason to suspect otherwise; see [Tips](https://shakenbreak.readthedocs.io/en/latest/Tips.html) )"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2da28e9e-6116-4e0c-9aea-f11ab6e20d01",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "To see the optional parameters that can be tuned in the distortion class, look at the docstrings: "
+ "```{tip}\n",
+ "You can restrict the ions that are distorted to a certain element using the keyword `distorted_elements`. \n",
+ "We can check it using the class attribute:\n",
+ "```"
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "7da2f948-a4a7-4003-8c1b-5a31e190dd8a",
+ "execution_count": 16,
+ "id": "52f9e11b",
"metadata": {
- "collapsed": true,
- "jupyter": {
- "outputs_hidden": true
- },
"pycharm": {
"name": "#%%\n"
- },
- "tags": []
+ }
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[0;31mInit signature:\u001b[0m\n",
- "\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDistortions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdefects_dict\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0moxidation_states\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdict_number_electrons_user\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdistortion_increment\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mbond_distortions\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mlocal_rattle\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mstdev\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdistorted_elements\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mDocstring:\u001b[0m \n",
- "Class to apply rattle and bond distortion to all defects in `defects_dict`\n",
- "(each defect as a pymatgen.analysis.defects.core.Defect() object).\n",
- "\u001b[0;31mInit docstring:\u001b[0m\n",
- "Args:\n",
- " defects_dict (:obj:`dict`):\n",
- " Dictionary of pymatgen.analysis.defects.core.Defect() objects.\n",
- " E.g.: {\n",
- " \"vacancies\": [Vacancy(), ...],\n",
- " \"interstitials\": [Interstitial(), ...],\n",
- " \"substitutions\": [Substitution(), ...],\n",
- " }\n",
- " In this case, folders will be name with the Defect.name() property.\n",
- " Alternatively, if specific defect/folder names are desired, these can be\n",
- " given as keys:\n",
- " {\n",
- " \"vacancies\": {\"vac_name\": Vacancy(), \"vac_2_name\": Vacancy()},\n",
- " \"interstitials\": {\"int_name\": Interstitial(), ...},\n",
- " \"substitutions\": {\"sub_name\": Substitution(), ...},\n",
- " }\n",
- " oxidation_states (:obj:`dict`):\n",
- " Dictionary of oxidation states for species in your material,\n",
- " used to determine the number of defect neighbours to distort\n",
- " (e.g {\"Cd\": +2, \"Te\": -2}). If none is provided, the oxidation\n",
- " states will be guessed based on the bulk composition and most\n",
- " common oxidation states of any extrinsic species.\n",
- " dict_number_electrons_user (:obj:`dict`):\n",
- " Optional argument to set the number of extra/missing charge\n",
- " (negative of electron count change) for the input defects\n",
- " in their neutral state, as a dictionary with format\n",
- " {'defect_name': charge_change} where charge_change is the\n",
- " negative of the number of extra/missing electrons.\n",
- " (Default: None)\n",
- " distortion_increment (:obj:`float`):\n",
- " Bond distortion increment. Distortion factors will range from\n",
- " 0 to +/-0.6, in increments of `distortion_increment`.\n",
- " Recommended values: 0.1-0.3\n",
- " (Default: 0.1)\n",
- " bond_distortions (:obj:`list`):\n",
- " List of bond distortions to apply to nearest neighbours,\n",
- " instead of the default set (e.g. [-0.5, 0.5]).\n",
- " (Default: None)\n",
- " local_rattle (:obj:`bool`):\n",
- " Whether to apply random displacements that tail off as we move\n",
- " away from the defect site. Not recommended as typically worsens\n",
- " performance. If False (default), all supercell sites are rattled\n",
- " with the same amplitude (full rattle).\n",
- " (Default: False)\n",
- " stdev (:obj:`float`):\n",
- " Standard deviation (in Angstroms) of the Gaussian distribution\n",
- " from which random atomic displacement distances are drawn during\n",
- " rattling. Recommended values: 0.25, or 0.15 for strongly-bound\n",
- " /ionic materials.\n",
- " (Default: 0.25)\n",
- " distorted_elements (:obj:`dict`):\n",
- " Optional argument to specify the neighbouring elements to\n",
- " distort for each defect, in the form of a dictionary with\n",
- " format {'defect_name': ['element1', 'element2', ...]}\n",
- " (e.g {'vac_1_Cd': ['Te']}). If None, the closest neighbours to\n",
- " the defect are chosen.\n",
- " (Default: None)\n",
- " **kwargs:\n",
- " Additional keyword arguments to pass to `hiphive`'s\n",
- " `mc_rattle` function. These include:\n",
- " - d_min (:obj:`float`):\n",
- " Minimum interatomic distance (in Angstroms). Monte Carlo rattle\n",
- " moves that put atoms at distances less than this will be heavily\n",
- " penalised.\n",
- " (Default: 2.25)\n",
- " - max_disp (:obj:`float`):\n",
- " Maximum atomic displacement (in Angstroms) during Monte Carlo\n",
- " rattling. Rarely occurs and is used primarily as a safety net.\n",
- " (Default: 2.0)\n",
- " - max_attempts (:obj:`int`):\n",
- " Limit for how many attempted rattle moves are allowed a single atom.\n",
- " - active_atoms (:obj:`list`):\n",
- " List of the atomic indices which should undergo Monte\n",
- " Carlo rattling. By default, all atoms are rattled.\n",
- " (Default: None)\n",
- " - seed (:obj:`int`):\n",
- " Seed for setting up NumPy random state from which random\n",
- " numbers are generated.\n",
- "\u001b[0;31mFile:\u001b[0m ~/Python_Modules/shakenbreak/shakenbreak/input.py\n",
- "\u001b[0;31mType:\u001b[0m type\n",
- "\u001b[0;31mSubclasses:\u001b[0m \n"
+ "User defined elements to distort: None\n"
]
}
],
"source": [
- "input.Distortions?"
+ "print(\"User defined elements to distort:\", Dist.distorted_elements)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c50bb0b",
+ "metadata": {},
+ "source": [
+ "If `None`, it means no restrictions, so nearest neighbours are distorted (recommended default, \n",
+ "unless you have reason to suspect otherwise; see [Tips](https://shakenbreak.readthedocs.io/en/latest/Tips.html) )"
]
},
{
@@ -801,7 +683,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 12,
"id": "87a095a8",
"metadata": {
"pycharm": {
@@ -815,11 +697,19 @@
"text": [
"Applying ShakeNBreak... Will apply the following bond distortions: ['-0.6', '-0.5', '-0.4', '-0.3', '-0.2', '-0.1', '0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6']. Then, will rattle with a std dev of 0.28 Å \n",
"\n",
- "\u001b[1m\n",
- "Defect: v_Cd\u001b[0m\n",
- "\u001b[1mNumber of missing electrons in neutral state: 2\u001b[0m\n",
+ "\u001B[1m\n",
+ "Defect: v_Cd_s0\u001B[0m\n",
+ "\u001B[1mNumber of missing electrons in neutral state: 2\u001B[0m\n",
"\n",
- "Defect v_Cd in charge state: 0. Number of distorted neighbours: 2\n"
+ "Defect v_Cd_s0 in charge state: -3. Number of distorted neighbours: 1\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: -2. Number of distorted neighbours: 0\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: -1. Number of distorted neighbours: 1\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: 0. Number of distorted neighbours: 2\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: +1. Number of distorted neighbours: 3\n"
]
}
],
@@ -837,7 +727,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 14,
"id": "f07375f6",
"metadata": {
"pycharm": {
@@ -854,7 +744,7 @@
}
],
"source": [
- "print(\"Keys for each defect entry:\", defects_dict[\"v_Cd\"].keys())"
+ "print(\"Keys for each defect entry:\", defects_dict[\"v_Cd_s0\"].keys())"
]
},
{
@@ -863,12 +753,12 @@
"metadata": {},
"source": [
"As well as the distorted structures for each charge state of all defects.\n",
- "We can access the distorted structures of v_Cd_0 like this:"
+ "We can access the distorted structures of v_Cd_s0_0 like this:"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 15,
"id": "e1903b20",
"metadata": {
"collapsed": true,
@@ -975,69 +865,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (0.6543, 0.6543, 12.4324) [0.0500, 0.0500, 0.9500]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (0.6543, 12.4324, 0.6543) [0.0500, 0.9500, 0.0500]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_-50.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1047,69 +937,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (0.8179, 0.8179, 12.2688) [0.0625, 0.0625, 0.9375]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (0.8179, 12.2688, 0.8179) [0.0625, 0.9375, 0.0625]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_-40.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1119,69 +1009,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (0.9815, 0.9815, 12.1052) [0.0750, 0.0750, 0.9250]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (0.9815, 12.1052, 0.9815) [0.0750, 0.9250, 0.0750]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_-30.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1191,69 +1081,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (1.1451, 1.1451, 11.9417) [0.0875, 0.0875, 0.9125]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (1.1451, 11.9417, 1.1451) [0.0875, 0.9125, 0.0875]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_-20.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1263,69 +1153,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (1.3087, 1.3087, 11.7781) [0.1000, 0.1000, 0.9000]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (1.3087, 11.7781, 1.3087) [0.1000, 0.9000, 0.1000]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_-10.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1335,69 +1225,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (1.4723, 1.4723, 11.6145) [0.1125, 0.1125, 0.8875]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (1.4723, 11.6145, 1.4723) [0.1125, 0.8875, 0.1125]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_0.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1407,69 +1297,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (1.6359, 1.6359, 11.4509) [0.1250, 0.1250, 0.8750]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (1.6359, 11.4509, 1.6359) [0.1250, 0.8750, 0.1250]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_10.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1479,69 +1369,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (1.7995, 1.7995, 11.2873) [0.1375, 0.1375, 0.8625]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (1.7995, 11.2873, 1.7995) [0.1375, 0.8625, 0.1375]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_20.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1551,69 +1441,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (1.9630, 1.9630, 11.1237) [0.1500, 0.1500, 0.8500]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (1.9630, 11.1237, 1.9630) [0.1500, 0.8500, 0.1500]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_30.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1623,69 +1513,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (2.1266, 2.1266, 10.9601) [0.1625, 0.1625, 0.8375]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (2.1266, 10.9601, 2.1266) [0.1625, 0.8375, 0.1625]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_40.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1695,69 +1585,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (2.2902, 2.2902, 10.7966) [0.1750, 0.1750, 0.8250]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (2.2902, 10.7966, 2.2902) [0.1750, 0.8250, 0.1750]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_50.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1767,69 +1657,69 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (2.4538, 2.4538, 10.6330) [0.1875, 0.1875, 0.8125]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (2.4538, 10.6330, 2.4538) [0.1875, 0.8125, 0.1875]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694],\n",
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067],\n",
" 'Bond_Distortion_60.0%': Structure Summary\n",
" Lattice\n",
" abc : 13.086768 13.086768 13.086768\n",
@@ -1839,79 +1729,79 @@
" B : 0.0 13.086768 0.0\n",
" C : 0.0 0.0 13.086768\n",
" pbc : True True True\n",
- " PeriodicSite: Cd2+ (0.2053, 0.2205, 6.5613) [0.0157, 0.0168, 0.5014]\n",
- " PeriodicSite: Cd2+ (0.3553, 6.4875, 0.1312) [0.0272, 0.4957, 0.0100]\n",
- " PeriodicSite: Cd2+ (-0.0190, 6.5182, 6.2570) [-0.0014, 0.4981, 0.4781]\n",
- " PeriodicSite: Cd2+ (6.1797, 0.2624, -0.2391) [0.4722, 0.0201, -0.0183]\n",
- " PeriodicSite: Cd2+ (6.3937, -0.0705, 6.3048) [0.4886, -0.0054, 0.4818]\n",
- " PeriodicSite: Cd2+ (6.6170, 6.7872, -0.3530) [0.5056, 0.5186, -0.0270]\n",
- " PeriodicSite: Cd2+ (6.2839, 6.8800, 6.8680) [0.4802, 0.5257, 0.5248]\n",
- " PeriodicSite: Cd2+ (-0.4558, 3.5713, 3.1244) [-0.0348, 0.2729, 0.2387]\n",
- " PeriodicSite: Cd2+ (-0.1384, 3.4883, 9.4875) [-0.0106, 0.2666, 0.7250]\n",
- " PeriodicSite: Cd2+ (-0.0991, 9.2566, 3.3244) [-0.0076, 0.7073, 0.2540]\n",
- " PeriodicSite: Cd2+ (-0.0666, 9.6259, 10.0500) [-0.0051, 0.7355, 0.7680]\n",
- " PeriodicSite: Cd2+ (6.5510, 3.6912, 3.0563) [0.5006, 0.2821, 0.2335]\n",
- " PeriodicSite: Cd2+ (6.7587, 3.3272, 9.5832) [0.5165, 0.2542, 0.7323]\n",
- " PeriodicSite: Cd2+ (6.6361, 9.8497, 3.7883) [0.5071, 0.7526, 0.2895]\n",
- " PeriodicSite: Cd2+ (6.6083, 10.0528, 9.9354) [0.5050, 0.7682, 0.7592]\n",
- " PeriodicSite: Cd2+ (3.0289, -0.1828, 3.5477) [0.2314, -0.0140, 0.2711]\n",
- " PeriodicSite: Cd2+ (3.1099, 0.0703, 9.6602) [0.2376, 0.0054, 0.7382]\n",
- " PeriodicSite: Cd2+ (3.5575, 6.4425, 3.2086) [0.2718, 0.4923, 0.2452]\n",
- " PeriodicSite: Cd2+ (3.4326, 6.7705, 9.5013) [0.2623, 0.5174, 0.7260]\n",
- " PeriodicSite: Cd2+ (9.7117, -0.4834, 3.4040) [0.7421, -0.0369, 0.2601]\n",
- " PeriodicSite: Cd2+ (10.0022, 0.1080, 9.6517) [0.7643, 0.0083, 0.7375]\n",
- " PeriodicSite: Cd2+ (10.1714, 6.6242, 3.1655) [0.7772, 0.5062, 0.2419]\n",
- " PeriodicSite: Cd2+ (9.3085, 6.4018, 9.7534) [0.7113, 0.4892, 0.7453]\n",
- " PeriodicSite: Cd2+ (3.3995, 3.4742, -0.1808) [0.2598, 0.2655, -0.0138]\n",
- " PeriodicSite: Cd2+ (3.7388, 3.4910, 6.3136) [0.2857, 0.2668, 0.4824]\n",
- " PeriodicSite: Cd2+ (3.3415, 9.5849, -0.3544) [0.2553, 0.7324, -0.0271]\n",
- " PeriodicSite: Cd2+ (3.7662, 9.4368, 6.4918) [0.2878, 0.7211, 0.4961]\n",
- " PeriodicSite: Cd2+ (10.1502, 3.5292, -0.1238) [0.7756, 0.2697, -0.0095]\n",
- " PeriodicSite: Cd2+ (10.0436, 3.1852, 6.9917) [0.7675, 0.2434, 0.5343]\n",
- " PeriodicSite: Cd2+ (10.0242, 9.9364, 0.1105) [0.7660, 0.7593, 0.0084]\n",
- " PeriodicSite: Cd2+ (9.7511, 9.8455, 6.4614) [0.7451, 0.7523, 0.4937]\n",
- " PeriodicSite: Te2- (1.5541, 1.9734, 4.9234) [0.1188, 0.1508, 0.3762]\n",
+ " PeriodicSite: Cd2+ (0.0896, 0.2795, 6.4999) [0.0068, 0.0214, 0.4967]\n",
+ " PeriodicSite: Cd2+ (0.0402, 6.9234, -0.0431) [0.0031, 0.5290, -0.0033]\n",
+ " PeriodicSite: Cd2+ (-0.1846, 6.6192, 6.3321) [-0.0141, 0.5058, 0.4839]\n",
+ " PeriodicSite: Cd2+ (6.6254, 0.2106, -0.3562) [0.5063, 0.0161, -0.0272]\n",
+ " PeriodicSite: Cd2+ (6.4710, 0.2184, 6.1241) [0.4945, 0.0167, 0.4680]\n",
+ " PeriodicSite: Cd2+ (7.0550, 6.7073, -0.1046) [0.5391, 0.5125, -0.0080]\n",
+ " PeriodicSite: Cd2+ (6.4008, 6.2229, 6.4269) [0.4891, 0.4755, 0.4911]\n",
+ " PeriodicSite: Cd2+ (0.0200, 3.1757, 3.1086) [0.0015, 0.2427, 0.2375]\n",
+ " PeriodicSite: Cd2+ (0.0873, 3.2997, 9.2876) [0.0067, 0.2521, 0.7097]\n",
+ " PeriodicSite: Cd2+ (0.1104, 9.4848, 3.0894) [0.0084, 0.7248, 0.2361]\n",
+ " PeriodicSite: Cd2+ (-0.2325, 9.4608, 9.9533) [-0.0178, 0.7229, 0.7606]\n",
+ " PeriodicSite: Cd2+ (6.4794, 3.3194, 3.0887) [0.4951, 0.2536, 0.2360]\n",
+ " PeriodicSite: Cd2+ (6.7217, 3.3565, 9.8048) [0.5136, 0.2565, 0.7492]\n",
+ " PeriodicSite: Cd2+ (6.4407, 9.7422, 3.4008) [0.4922, 0.7444, 0.2599]\n",
+ " PeriodicSite: Cd2+ (6.9061, 9.6376, 9.5437) [0.5277, 0.7364, 0.7293]\n",
+ " PeriodicSite: Cd2+ (3.3935, -0.1058, 3.6500) [0.2593, -0.0081, 0.2789]\n",
+ " PeriodicSite: Cd2+ (3.4617, 0.1110, 9.7840) [0.2645, 0.0085, 0.7476]\n",
+ " PeriodicSite: Cd2+ (3.5704, 6.2570, 3.1834) [0.2728, 0.4781, 0.2433]\n",
+ " PeriodicSite: Cd2+ (3.0190, 6.6468, 10.2011) [0.2307, 0.5079, 0.7795]\n",
+ " PeriodicSite: Cd2+ (9.3425, 0.0365, 2.8721) [0.7139, 0.0028, 0.2195]\n",
+ " PeriodicSite: Cd2+ (10.0866, 0.1205, 10.0806) [0.7707, 0.0092, 0.7703]\n",
+ " PeriodicSite: Cd2+ (9.6178, 6.6634, 3.4234) [0.7349, 0.5092, 0.2616]\n",
+ " PeriodicSite: Cd2+ (9.6378, 6.4346, 9.4803) [0.7365, 0.4917, 0.7244]\n",
+ " PeriodicSite: Cd2+ (3.6548, 3.1972, -0.3796) [0.2793, 0.2443, -0.0290]\n",
+ " PeriodicSite: Cd2+ (3.7174, 3.5616, 6.3047) [0.2841, 0.2722, 0.4818]\n",
+ " PeriodicSite: Cd2+ (3.2182, 10.2993, 0.1662) [0.2459, 0.7870, 0.0127]\n",
+ " PeriodicSite: Cd2+ (3.3251, 9.9734, 6.4778) [0.2541, 0.7621, 0.4950]\n",
+ " PeriodicSite: Cd2+ (9.7856, 3.4538, 0.3456) [0.7477, 0.2639, 0.0264]\n",
+ " PeriodicSite: Cd2+ (10.4978, 3.5281, 7.0212) [0.8022, 0.2696, 0.5365]\n",
+ " PeriodicSite: Cd2+ (9.7876, 9.7389, 0.0448) [0.7479, 0.7442, 0.0034]\n",
+ " PeriodicSite: Cd2+ (9.9345, 9.7646, 6.8041) [0.7591, 0.7461, 0.5199]\n",
+ " PeriodicSite: Te2- (1.5473, 1.9801, 5.3948) [0.1182, 0.1513, 0.4122]\n",
" PeriodicSite: Te2- (2.6174, 2.6174, 10.4694) [0.2000, 0.2000, 0.8000]\n",
- " PeriodicSite: Te2- (1.7175, 8.2820, 5.4998) [0.1312, 0.6329, 0.4203]\n",
- " PeriodicSite: Te2- (1.4773, 8.0915, 11.0885) [0.1129, 0.6183, 0.8473]\n",
- " PeriodicSite: Te2- (8.2653, 1.6044, 4.8425) [0.6316, 0.1226, 0.3700]\n",
- " PeriodicSite: Te2- (8.2235, 1.4452, 11.0741) [0.6284, 0.1104, 0.8462]\n",
- " PeriodicSite: Te2- (8.1547, 8.4171, 4.9777) [0.6231, 0.6432, 0.3804]\n",
- " PeriodicSite: Te2- (8.0257, 8.4326, 11.6793) [0.6133, 0.6444, 0.8925]\n",
- " PeriodicSite: Te2- (1.4910, 4.8183, 1.9866) [0.1139, 0.3682, 0.1518]\n",
- " PeriodicSite: Te2- (1.6639, 4.7990, 7.8885) [0.1271, 0.3667, 0.6028]\n",
+ " PeriodicSite: Te2- (1.6933, 7.5982, 4.6149) [0.1294, 0.5806, 0.3526]\n",
+ " PeriodicSite: Te2- (1.1356, 8.3084, 11.5531) [0.0868, 0.6349, 0.8828]\n",
+ " PeriodicSite: Te2- (7.8890, 2.1691, 4.8407) [0.6028, 0.1657, 0.3699]\n",
+ " PeriodicSite: Te2- (8.2757, 2.2192, 11.2885) [0.6324, 0.1696, 0.8626]\n",
+ " PeriodicSite: Te2- (8.2035, 8.1637, 4.9887) [0.6269, 0.6238, 0.3812]\n",
+ " PeriodicSite: Te2- (8.3141, 8.1945, 11.4733) [0.6353, 0.6262, 0.8767]\n",
+ " PeriodicSite: Te2- (2.0153, 4.6415, 2.0961) [0.1540, 0.3547, 0.1602]\n",
+ " PeriodicSite: Te2- (1.6867, 4.9934, 8.3357) [0.1289, 0.3816, 0.6370]\n",
" PeriodicSite: Te2- (2.6174, 10.4694, 2.6174) [0.2000, 0.8000, 0.2000]\n",
- " PeriodicSite: Te2- (1.6669, 11.6722, 8.4278) [0.1274, 0.8919, 0.6440]\n",
- " PeriodicSite: Te2- (8.0377, 4.8619, 1.4752) [0.6142, 0.3715, 0.1127]\n",
- " PeriodicSite: Te2- (8.0790, 5.0655, 7.4358) [0.6173, 0.3871, 0.5682]\n",
- " PeriodicSite: Te2- (7.9597, 11.5680, 1.4783) [0.6082, 0.8839, 0.1130]\n",
- " PeriodicSite: Te2- (8.8232, 11.9197, 7.9344) [0.6742, 0.9108, 0.6063]\n",
- " PeriodicSite: Te2- (4.7196, 1.6713, 2.1132) [0.3606, 0.1277, 0.1615]\n",
- " PeriodicSite: Te2- (4.9108, 1.4912, 7.7656) [0.3753, 0.1139, 0.5934]\n",
- " PeriodicSite: Te2- (5.3145, 7.9556, 1.4580) [0.4061, 0.6079, 0.1114]\n",
- " PeriodicSite: Te2- (4.9771, 8.4099, 8.3701) [0.3803, 0.6426, 0.6396]\n",
- " PeriodicSite: Te2- (11.7093, 1.8306, 1.2709) [0.8947, 0.1399, 0.0971]\n",
- " PeriodicSite: Te2- (11.8438, 1.6705, 7.7841) [0.9050, 0.1276, 0.5948]\n",
- " PeriodicSite: Te2- (11.7240, 8.1713, 1.3777) [0.8959, 0.6244, 0.1053]\n",
- " PeriodicSite: Te2- (11.7746, 8.1927, 8.2275) [0.8997, 0.6260, 0.6287]\n",
- " PeriodicSite: Te2- (5.1086, 4.9076, 4.8338) [0.3904, 0.3750, 0.3694]\n",
- " PeriodicSite: Te2- (5.0266, 4.9282, 11.3881) [0.3841, 0.3766, 0.8702]\n",
- " PeriodicSite: Te2- (4.9662, 11.5871, 4.8092) [0.3795, 0.8854, 0.3675]\n",
- " PeriodicSite: Te2- (5.1264, 11.1806, 11.4959) [0.3917, 0.8543, 0.8784]\n",
- " PeriodicSite: Te2- (11.5396, 4.9907, 5.2762) [0.8818, 0.3814, 0.4032]\n",
- " PeriodicSite: Te2- (11.5086, 5.1651, 11.4601) [0.8794, 0.3947, 0.8757]\n",
- " PeriodicSite: Te2- (11.5595, 11.8954, 4.7469) [0.8833, 0.9090, 0.3627]\n",
- " PeriodicSite: Te2- (11.0723, 11.6486, 11.3780) [0.8461, 0.8901, 0.8694]}}"
+ " PeriodicSite: Te2- (1.4065, 11.7689, 8.2027) [0.1075, 0.8993, 0.6268]\n",
+ " PeriodicSite: Te2- (8.1303, 4.9487, 1.5344) [0.6213, 0.3781, 0.1172]\n",
+ " PeriodicSite: Te2- (8.3875, 4.5897, 7.8657) [0.6409, 0.3507, 0.6010]\n",
+ " PeriodicSite: Te2- (7.8168, 11.6132, 1.6491) [0.5973, 0.8874, 0.1260]\n",
+ " PeriodicSite: Te2- (8.2167, 11.6827, 8.4441) [0.6279, 0.8927, 0.6452]\n",
+ " PeriodicSite: Te2- (5.0518, 1.4113, 1.8714) [0.3860, 0.1078, 0.1430]\n",
+ " PeriodicSite: Te2- (4.1490, 1.7993, 7.7967) [0.3170, 0.1375, 0.5958]\n",
+ " PeriodicSite: Te2- (4.5296, 8.0483, 1.4785) [0.3461, 0.6150, 0.1130]\n",
+ " PeriodicSite: Te2- (4.6569, 7.9389, 7.6658) [0.3559, 0.6066, 0.5858]\n",
+ " PeriodicSite: Te2- (11.2656, 2.0771, 1.9398) [0.8608, 0.1587, 0.1482]\n",
+ " PeriodicSite: Te2- (11.3931, 1.8111, 8.4850) [0.8706, 0.1384, 0.6484]\n",
+ " PeriodicSite: Te2- (11.5762, 8.4324, 1.1160) [0.8846, 0.6443, 0.0853]\n",
+ " PeriodicSite: Te2- (11.4065, 7.9827, 8.3422) [0.8716, 0.6100, 0.6375]\n",
+ " PeriodicSite: Te2- (4.8087, 4.5397, 4.5315) [0.3674, 0.3469, 0.3463]\n",
+ " PeriodicSite: Te2- (5.2584, 4.5541, 11.6870) [0.4018, 0.3480, 0.8930]\n",
+ " PeriodicSite: Te2- (4.6601, 11.4685, 5.0872) [0.3561, 0.8763, 0.3887]\n",
+ " PeriodicSite: Te2- (4.7645, 11.6046, 11.6746) [0.3641, 0.8867, 0.8921]\n",
+ " PeriodicSite: Te2- (11.6860, 4.7569, 4.8917) [0.8930, 0.3635, 0.3738]\n",
+ " PeriodicSite: Te2- (11.7206, 5.0995, 11.6945) [0.8956, 0.3897, 0.8936]\n",
+ " PeriodicSite: Te2- (11.8344, 11.3313, 4.8996) [0.9043, 0.8659, 0.3744]\n",
+ " PeriodicSite: Te2- (11.3554, 10.9915, 11.8662) [0.8677, 0.8399, 0.9067]}}"
]
},
- "execution_count": 21,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"\\nUndistorted and distorted structures:\")\n",
- "defects_dict[\"v_Cd\"][\"charges\"][0][\"structures\"]"
+ "defects_dict[\"v_Cd_s0\"][\"charges\"][0][\"structures\"]"
]
},
{
@@ -1948,7 +1838,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 16,
"id": "972dac91",
"metadata": {
"pycharm": {
@@ -1965,11 +1855,19 @@
"text": [
"Applying ShakeNBreak... Will apply the following bond distortions: ['-0.6', '-0.5', '-0.4', '-0.3', '-0.2', '-0.1', '0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6']. Then, will rattle with a std dev of 0.28 Å \n",
"\n",
- "\u001b[1m\n",
- "Defect: v_Cd\u001b[0m\n",
- "\u001b[1mNumber of missing electrons in neutral state: 2\u001b[0m\n",
+ "\u001B[1m\n",
+ "Defect: v_Cd_s0\u001B[0m\n",
+ "\u001B[1mNumber of missing electrons in neutral state: 2\u001B[0m\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: -3. Number of distorted neighbours: 1\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: -2. Number of distorted neighbours: 0\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: -1. Number of distorted neighbours: 1\n",
"\n",
- "Defect v_Cd in charge state: 0. Number of distorted neighbours: 2\n"
+ "Defect v_Cd_s0 in charge state: 0. Number of distorted neighbours: 2\n",
+ "\n",
+ "Defect v_Cd_s0 in charge state: +1. Number of distorted neighbours: 3\n"
]
}
],
@@ -1996,7 +1894,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 17,
"id": "50d39076-a262-4eac-84df-27ef74def826",
"metadata": {
"pycharm": {
@@ -2041,7 +1939,7 @@
}
],
"source": [
- "!cat ./v_Cd_0/Bond_Distortion_-10.0%/INCAR"
+ "!cat ./v_Cd_s0_0/Bond_Distortion_-10.0%/INCAR"
]
},
{
@@ -2116,11 +2014,11 @@
"source": [
"#### a) For `VASP` users:\n",
"\n",
- "Then parse the energies obtained by running the `snb-parse` command from the top-level folder containing your defect folders (e.g. `v_Cd_0` etc. (with subfolders: `v_Cd_0/Bond_Distortion_10.0%` etc.)). This will parse the energies and store them in a `v_Cd_0.yaml` etc file in the defect folders, to allow easy plotting and analysis.\n",
+ "Then parse the energies obtained by running the `snb-parse` command from the top-level folder containing your defect folders (e.g. `v_Cd_s0_0` etc. (with subfolders: `v_Cd_s0_0/Bond_Distortion_10.0%` etc.)). This will parse the energies and store them in a `v_Cd_s0_0.yaml` etc file in the defect folders, to allow easy plotting and analysis.\n",
"\n",
"It is also recommended to parse the final structures (`CONTCAR`s files if using `VASP`) obtained with each distortion relaxation for further structural analysis, which is done automatically when downloaded to your local folders as below. \n",
"\n",
- "Copying these data to your local PC can be done quickly from your local folder top-level folder (containing `v_Cd_0` etc) with the following code:\n",
+ "Copying these data to your local PC can be done quickly from your local folder top-level folder (containing `v_Cd_s0_0` etc) with the following code:\n",
"\n",
"```bash\n",
"for defect in ./*{_,_-}[0-9]/; do cd $defect; \n",
@@ -2142,7 +2040,7 @@
},
"source": [
"#### b) If using `CP2K`, `Quantum Espresso`, `CASTEP` or `FHI-aims`:\n",
- "Then parse the energies obtained by running the `snb-parse` command from the top-level folder containing your defect folders (e.g. `v_Cd_0` etc. (with subfolders: `v_Cd_0/Bond_Distortion_10.0%` etc.)) and setting the `--code` option (e.g. `snb-parse --code cp2k`). This will parse the energies and store them in a `v_Cd_0.yaml` etc file in the defect folders, to allow easy plotting and analysis. \n",
+ "Then parse the energies obtained by running the `snb-parse` command from the top-level folder containing your defect folders (e.g. `v_Cd_s0_0` etc. (with subfolders: `v_Cd_s0_0/Bond_Distortion_10.0%` etc.)) and setting the `--code` option (e.g. `snb-parse --code cp2k`). This will parse the energies and store them in a `v_Cd_s0_0.yaml` etc file in the defect folders, to allow easy plotting and analysis. \n",
"\n",
"It is also recommended to parse the final structures obtained with each relaxation for further structural analysis. Depending on the code the structure information is read from:\n",
"* `CP2K`: restart file\n",
@@ -2166,7 +2064,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 18,
"id": "1ddbe86f-a21d-4bc0-b95d-0a8373258f53",
"metadata": {
"pycharm": {
@@ -2175,8 +2073,8 @@
},
"outputs": [],
"source": [
- "!cp -r ../tests/data/example_results/v_Cd* .\n",
- "!cp ../tests/data/vasp/CdTe/distortion_metadata.json .\n",
+ "!rm -r ./v_Cd_s0*\n",
+ "!cp -r ../tests/data/example_results/v_Cd_s0* .\n",
"# may need to change path if you've moved the example notebook elsewhere"
]
},
@@ -2195,7 +2093,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 19,
"id": "7398d15b-498a-448a-a3d4-f555d1641ec9",
"metadata": {
"pycharm": {
@@ -2209,7 +2107,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 20,
"id": "cadb8e69-f8e5-47c1-ae38-60d47c42b17b",
"metadata": {
"pycharm": {
@@ -2223,19 +2121,18 @@
"output_type": "stream",
"text": [
"\n",
- "v_Cd\n",
- "v_Cd_-1: Energy difference between minimum, found with 0.2 bond distortion, and unperturbed: -0.90 eV.\n",
- "Energy lowering distortion found for v_Cd with charge -1. Adding to low_energy_defects dictionary.\n",
- "No energy lowering distortion with energy difference greater than min_e_diff = 0.05 eV found for v_Cd with charge -2.\n",
- "v_Cd_0: Energy difference between minimum, found with -0.6 bond distortion, and unperturbed: -0.76 eV.\n",
+ "v_Cd_s0\n",
+ "Parsing v_Cd_s0_-2...\n",
+ "No energy lowering distortion with energy difference greater than min_e_diff = 0.05 eV found for v_Cd_s0 with charge -2.\n",
+ "Parsing v_Cd_s0_0...\n",
+ "v_Cd_s0_0: Energy difference between minimum, found with -0.6 bond distortion, and unperturbed: -0.76 eV.\n",
+ "Energy lowering distortion found for v_Cd_s0 with charge 0. Adding to low_energy_defects dictionary.\n",
+ "Parsing v_Cd_s0_-1...\n",
+ "v_Cd_s0_-1: Energy difference between minimum, found with 0.2 bond distortion, and unperturbed: -0.90 eV.\n",
"Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "New (according to structure matching) low-energy distorted structure found for v_Cd_0, adding to low_energy_defects['v_Cd'] list.\n",
+ "New (according to structure matching) low-energy distorted structure found for v_Cd_s0_-1, adding to low_energy_defects['v_Cd_s0'] list.\n",
"\n",
- "Comparing and pruning defect structures across charge states...\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n"
+ "Comparing and pruning defect structures across charge states...\n"
]
}
],
@@ -2258,7 +2155,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 22,
"id": "7c1b4b76-3015-43a1-acc6-e3974d92ba83",
"metadata": {
"pycharm": {
@@ -2270,81 +2167,40 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Plot saved to v_Cd_-1/v_Cd_-1.svg\n",
- "Plot saved to v_Cd_0/v_Cd_0.svg\n"
+ "Energy lowering distortion found for v_Cd_s0 with charge 0. Generating distortion plot...\n",
+ "Plot saved to v_Cd_s0_0/v_Cd_s0_0.png\n",
+ "Energy lowering distortion found for v_Cd_s0 with charge -1. Generating distortion plot...\n",
+ "Plot saved to v_Cd_s0_-1/v_Cd_s0_-1.png\n"
]
},
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "plotting.py:1854: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n"
- ]
- }
- ],
- "source": [
- "figs = plotting.plot_all_defects(defect_charges_dict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "id": "7c1b4b76-3015-43a1-acc6-e3974d92ba83",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
{
"data": {
- "image/svg+xml": "",
+ "image/png": "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\n",
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 56,
"metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Show plots\n",
- "from IPython.core.display import SVG\n",
- "SVG(filename=\"./v_Cd_0/v_Cd_0.svg\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 57,
- "id": "7c1b4b76-3015-43a1-acc6-e3974d92ba83",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
+ "output_type": "display_data"
+ },
{
"data": {
- "image/svg+xml": "",
+ "image/png": "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\n",
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 57,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "# Show plots\n",
- "from IPython.core.display import SVG\n",
- "SVG(filename=\"./v_Cd_-1/v_Cd_-1.svg\")"
+ "figs = plotting.plot_all_defects(defect_charges_dict)"
]
},
{
"cell_type": "markdown",
- "id": "10d1951b-e433-4890-9495-e04b1d38a0e0",
+ "id": "e6283091",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2356,12 +2212,11 @@
},
{
"cell_type": "markdown",
- "id": "fed3f81c-d447-40aa-a35c-1b0441173460",
+ "id": "b8e99db1",
"metadata": {
"pycharm": {
"name": "#%% md\n"
- },
- "tags": []
+ }
},
"source": [
"### Can also add a colorbar \n",
@@ -2375,27 +2230,18 @@
},
{
"cell_type": "code",
- "execution_count": 58,
- "id": "8c4ab021-6a67-4429-86d7-7606edbf4992",
+ "execution_count": null,
+ "id": "731b3655",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Comparing structures to Unperturbed...\n",
- "Previous version of v_Cd_-1.svg found in output_path: 'v_Cd_-1/'. Will rename old plot to v_Cd_-1_2022-10-19-11-42.svg.\n",
- "Plot saved to v_Cd_-1/v_Cd_-1.svg\n",
- "Comparing structures to Unperturbed...\n",
- "Previous version of v_Cd_0.svg found in output_path: 'v_Cd_0/'. Will rename old plot to v_Cd_0_2022-10-19-11-42.svg.\n",
- "Plot saved to v_Cd_0/v_Cd_0.svg\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"figs = plotting.plot_all_defects(\n",
" defect_charges_dict,\n",
@@ -2405,55 +2251,18 @@
},
{
"cell_type": "code",
- "execution_count": 59,
- "id": "8c4ab021-6a67-4429-86d7-7606edbf4992",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "data": {
- "image/svg+xml": "",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Show plots\n",
- "from IPython.core.display import SVG\n",
- "SVG(filename=\"./v_Cd_0/v_Cd_0.svg\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "6a7bd7f6-b558-4640-b613-226b3e625494",
+ "execution_count": null,
+ "id": "7b17c125",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Comparing structures to Unperturbed...\n",
- "Previous version of v_Cd_-1.svg found in output_path: 'v_Cd_-1/'. Will rename old plot to v_Cd_-1_2022-10-19-11-42.svg.\n",
- "Plot saved to v_Cd_-1/v_Cd_-1.svg\n",
- "Comparing structures to Unperturbed...\n",
- "Previous version of v_Cd_0.svg found in output_path: 'v_Cd_0/'. Will rename old plot to v_Cd_0_2022-10-19-11-42.svg.\n",
- "Plot saved to v_Cd_0/v_Cd_0.svg\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"figs = plotting.plot_all_defects(\n",
" defect_charges_dict,\n",
@@ -2462,33 +2271,9 @@
")"
]
},
- {
- "cell_type": "code",
- "execution_count": 61,
- "id": "609155b8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": "",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Show plots\n",
- "from IPython.core.display import SVG\n",
- "SVG(filename=\"./v_Cd_-1/v_Cd_-1.svg\")"
- ]
- },
{
"cell_type": "markdown",
- "id": "8ff48ae2-68d1-4ad5-8a2d-7fe60cb5af18",
+ "id": "06cd22c2",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2502,7 +2287,7 @@
},
{
"cell_type": "markdown",
- "id": "64bbd1d9-3ae3-490d-a8b6-a3351563413b",
+ "id": "f252aa7d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2514,7 +2299,7 @@
},
{
"cell_type": "markdown",
- "id": "6ca018a4-ccb6-4ba0-985b-e748eee08a27",
+ "id": "61a51d49",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2526,31 +2311,20 @@
},
{
"cell_type": "code",
- "execution_count": 62,
- "id": "b8b11819-774f-4ce8-8a0b-4cc765558f2d",
+ "execution_count": null,
+ "id": "67199c62",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Energy lowering distortion number 0\n",
- "Found for charge states: [-1]\n",
- "Not found in: {0, -2} \n",
- "\n",
- "Energy lowering distortion number 1\n",
- "Found for charge states: [0]\n",
- "Not found in: {-1, -2} \n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "for index, subdict in enumerate(low_energy_defects[\"v_Cd\"]):\n",
+ "for index, subdict in enumerate(low_energy_defects[\"v_Cd_s0\"]):\n",
" print(f\"Energy lowering distortion number {index}\")\n",
" print(\"Found for charge states:\", subdict[\"charges\"]) # Charge state for which the energy lowering was found\n",
" print(f\"Not found in:\", subdict[\"excluded_charges\"], \"\\n\")"
@@ -2558,7 +2332,7 @@
},
{
"cell_type": "markdown",
- "id": "a402b4a6-7380-4522-a686-f2880edd84eb",
+ "id": "2330e0cf",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2570,36 +2344,30 @@
},
{
"cell_type": "code",
- "execution_count": 63,
- "id": "4b6ea78a-48b6-48d3-856d-e059aaf1f924",
+ "execution_count": null,
+ "id": "1468a763",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Writing low-energy distorted structure to ./v_Cd_0/Bond_Distortion_20.0%_from_-1\n",
- "No subfolders with VASP input files found in ./v_Cd_0, so just writing distorted POSCAR file to ./v_Cd_0/Bond_Distortion_20.0%_from_-1 directory.\n",
- "Writing low-energy distorted structure to ./v_Cd_-2/Bond_Distortion_20.0%_from_-1\n",
- "No subfolders with VASP input files found in ./v_Cd_-2, so just writing distorted POSCAR file to ./v_Cd_-2/Bond_Distortion_20.0%_from_-1 directory.\n",
- "As ./v_Cd_-1/Bond_Distortion_-60.0%_from_0 already exists, it's assumed this structure has already been tested. Skipping...\n",
- "Writing low-energy distorted structure to ./v_Cd_-2/Bond_Distortion_-60.0%_from_0\n",
- "No subfolders with VASP input files found in ./v_Cd_-2, so just writing distorted POSCAR file to ./v_Cd_-2/Bond_Distortion_-60.0%_from_0 directory.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "energy_lowering_distortions.write_distorted_inputs(low_energy_defects)"
+ "energy_lowering_distortions.write_retest_inputs(low_energy_defects)"
]
},
{
"cell_type": "markdown",
- "id": "614a2084-e6b9-48ab-9a56-2e07bf0b497d",
- "metadata": {},
+ "id": "5c48b7d6",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"```{Note}\n",
"Note here the nomenclature we use for the distorted structures we've imported from other charge states (i.e. `Bond_Distortion_-60.0%_from_0` refers to the structure obtained from relaxing the -60% distortion of the neutral (q = 0) charge state).\n",
@@ -2608,8 +2376,12 @@
},
{
"cell_type": "markdown",
- "id": "76e0728a-6e89-4f9b-9436-718d94d7bfb3",
- "metadata": {},
+ "id": "5cc78b6c",
+ "metadata": {
+ "pycharm": {
+ "name": "#%% md\n"
+ }
+ },
"source": [
"We can send these additional test distortions to the HPCs using this `bash` code:\n",
"```bash\n",
@@ -2623,7 +2395,7 @@
},
{
"cell_type": "markdown",
- "id": "7d09bbe6-693c-4c15-8586-3c9c13e78426",
+ "id": "2ac402c4",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2635,27 +2407,31 @@
},
{
"cell_type": "code",
- "execution_count": 64,
- "id": "7eb4d411-fc2b-4680-92a9-c3c683898814",
+ "execution_count": null,
+ "id": "3cbf3c6e",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
- "# !cp ./v_Cd_0/v_Cd_0_additional_distortions.yaml ./v_Cd_0/v_Cd_0.yaml\n",
- "# !cp ./v_Cd_-1/v_Cd_-1_additional_distortions.yaml ./v_Cd_-1/v_Cd_-1.yaml\n",
- "# !cp ./v_Cd_-2/v_Cd_-2_additional_distortions.yaml ./v_Cd_-2/v_Cd_-2.yaml\n",
- "# !cp ./v_Cd_0/Bond_Distortion_-60.0%/CONTCAR ./v_Cd_-1/Bond_Distortion_-60.0%_from_0/\n",
- "# !cp ./v_Cd_0/Bond_Distortion_-60.0%/CONTCAR ./v_Cd_-2/Bond_Distortion_-60.0%_from_0/\n",
- "# !cp ./v_Cd_-1/Unperturbed/CONTCAR ./v_Cd_-2/Bond_Distortion_20.0%_from_-1/\n",
- "# !cp ./v_Cd_-1/Unperturbed/CONTCAR ./v_Cd_0/Bond_Distortion_20.0%_from_-1/"
+ "!cp ./v_Cd_s0_0/v_Cd_s0_0_additional_distortions.yaml ./v_Cd_s0_0/v_Cd_s0_0.yaml\n",
+ "!cp ./v_Cd_s0_-1/v_Cd_s0_-1_additional_distortions.yaml ./v_Cd_s0_-1/v_Cd_s0_-1.yaml\n",
+ "!cp ./v_Cd_s0_-2/v_Cd_s0_-2_additional_distortions.yaml ./v_Cd_s0_-2/v_Cd_s0_-2.yaml\n",
+ "!cp ./v_Cd_s0_0/Bond_Distortion_-60.0%/CONTCAR ./v_Cd_s0_-1/Bond_Distortion_-60.0%_from_0/\n",
+ "!cp ./v_Cd_s0_0/Bond_Distortion_-60.0%/CONTCAR ./v_Cd_s0_-2/Bond_Distortion_-60.0%_from_0/\n",
+ "!cp ./v_Cd_s0_-1/Unperturbed/CONTCAR ./v_Cd_s0_-2/Bond_Distortion_20.0%_from_-1/\n",
+ "!cp ./v_Cd_s0_-1/Unperturbed/CONTCAR ./v_Cd_s0_0/Bond_Distortion_20.0%_from_-1/"
]
},
{
"cell_type": "markdown",
- "id": "ff3434e0-fd91-4d00-bd6a-01e47b78524a",
+ "id": "d25f09d1",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2667,48 +2443,25 @@
},
{
"cell_type": "code",
- "execution_count": 65,
- "id": "da6b9e3b-c05d-4dcd-a281-0b972ea56c05",
+ "execution_count": null,
+ "id": "39f39083",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
- },
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "v_Cd\n",
- "v_Cd_-1: Energy difference between minimum, found with -60.0%_from_0 bond distortion, and unperturbed: -1.20 eV.\n",
- "Energy lowering distortion found for v_Cd with charge -1. Adding to low_energy_defects dictionary.\n",
- "v_Cd_-2: Energy difference between minimum, found with 20.0%_from_-1 bond distortion, and unperturbed: -1.90 eV.\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "New (according to structure matching) low-energy distorted structure found for v_Cd_-2, adding to low_energy_defects['v_Cd'] list.\n",
- "v_Cd_0: Energy difference between minimum, found with -0.6 bond distortion, and unperturbed: -0.76 eV.\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Low-energy distorted structure for v_Cd_0 already found with charge states [-1], storing together.\n",
- "\n",
- "Comparing and pruning defect structures across charge states...\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Ground-state structure found for v_Cd with charges [-1, 0] has been also previously been found for charge state -2 (according to structure matching). Adding this charge to the corresponding entry in low_energy_defects[v_Cd].\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Ground-state structure found for v_Cd with charges [-2] has been also previously been found for charge state 0 (according to structure matching). Adding this charge to the corresponding entry in low_energy_defects[v_Cd].\n",
- "Comparing structures to specified ref_structure (Cd31 Te32)...\n",
- "Ground-state structure found for v_Cd with charges [-2, 0] has been also previously been found for charge state -1 (according to structure matching). Adding this charge to the corresponding entry in low_energy_defects[v_Cd].\n"
- ]
}
- ],
+ },
+ "outputs": [],
"source": [
"low_energy_defects = energy_lowering_distortions.get_energy_lowering_distortions(defect_charges_dict)"
]
},
{
"cell_type": "markdown",
- "id": "2723ed3d-d108-4716-b7ea-cae91e771910",
+ "id": "805f4d4f",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2720,174 +2473,95 @@
},
{
"cell_type": "code",
- "execution_count": 66,
- "id": "c9b9f5d3-cce7-4d26-99ed-e8049127bab5",
+ "execution_count": null,
+ "id": "33df4aab",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Previous version of v_Cd_-1.svg found in output_path: 'v_Cd_-1/'. Will rename old plot to v_Cd_-1_2022-10-19-11-43.svg.\n",
- "Plot saved to v_Cd_-1/v_Cd_-1.svg\n",
- "Plot saved to v_Cd_-2/v_Cd_-2.svg\n",
- "Previous version of v_Cd_0.svg found in output_path: 'v_Cd_0/'. Will rename old plot to v_Cd_0_2022-10-19-11-43.svg.\n",
- "Plot saved to v_Cd_0/v_Cd_0.svg\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"figs = plotting.plot_all_defects(defect_charges_dict)"
]
},
{
- "cell_type": "code",
- "execution_count": 67,
- "id": "c9b9f5d3-cce7-4d26-99ed-e8049127bab5",
+ "cell_type": "markdown",
+ "id": "3b52c9b1",
"metadata": {
"pycharm": {
- "name": "#%%\n"
+ "name": "#%% md\n"
}
},
- "outputs": [
- {
- "data": {
- "image/svg+xml": "",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 67,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
- "SVG(filename=\"./v_Cd_-2/v_Cd_-2.svg\")"
+ "In this example case, for VCd0 the distorted structure originally found for the -1 charge state comes out lower energy than the VCd0 unperturbed relaxation, but still higher energy than the previously identified ground-state at -0.3, -0.4 and -0.6 distortion factors. \n",
+ "\n",
+ "For VCd-1, the distorted structure originally found for the neutral (0) charge state comes out lower energy than the previously identified ground-state at distortion factors >0.2.\n",
+ "\n",
+ "We now continue our defect calculations using the ground-state `CONTCAR`s we've obtained for each defect, with our fully-converged `INCAR` and `KPOINTS` settings, to get our final defect formation energies (confident that we've identified the ground-state defect structure!). The `energy_lowering_distortions.write_groundstate_structure()` function automatically writes these lowest-energy structures to our defect folders:"
]
},
{
"cell_type": "code",
- "execution_count": 68,
- "id": "c9b9f5d3-cce7-4d26-99ed-e8049127bab5",
+ "execution_count": null,
+ "id": "ebb87933",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "data": {
- "image/svg+xml": "",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 68,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "SVG(filename=\"./v_Cd_-1/v_Cd_-1.svg\")"
+ "energy_lowering_distortions.write_groundstate_structure()"
]
},
{
"cell_type": "code",
- "execution_count": 69,
- "id": "c9b9f5d3-cce7-4d26-99ed-e8049127bab5",
+ "execution_count": null,
+ "id": "6e0c6282",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "data": {
- "image/svg+xml": "",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "SVG(filename=\"./v_Cd_0/v_Cd_0.svg\")"
+ "!head v_Cd_s0_0/groundstate_POSCAR # groundstate structure from -60% distortion relaxation"
]
},
{
- "cell_type": "markdown",
- "id": "198c0f9a-b320-4c64-8951-b7f7362d9a3b",
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3f12096",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
- "name": "#%% md\n"
+ "name": "#%%\n"
}
},
- "source": [
- "In this example case, for VCd0 the distorted structure originally found for the -1 charge state comes out lower energy than the VCd0 unperturbed relaxation, but still higher energy than the previously identified ground-state at -0.3, -0.4 and -0.6 distortion factors. \n",
- "\n",
- "For VCd-1, the distorted structure originally found for the neutral (0) charge state comes out lower energy than the previously identified ground-state at distortion factors >0.2.\n",
- "\n",
- "We now continue our defect calculations using the ground-state `CONTCAR`s we've obtained for each defect, with our fully-converged `INCAR` and `KPOINTS` settings, to get our final defect formation energies (confident that we've identified the ground-state defect structure!). The `energy_lowering_distortions.write_groundstate_structure()` function automatically writes these lowest-energy structures to our defect folders:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "id": "a75f2092-edb0-46fd-aa5d-8a50aaaea253",
- "metadata": {},
- "outputs": [],
- "source": [
- "energy_lowering_distortions.write_groundstate_structure()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "id": "d8112dc1-fe01-4ee4-afaf-fc06eba8eb58",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-60.0%_Bond__vac_1_Cd[0. 0. 0.]_-dNELECT\n",
- " 1.0000000000000000 \n",
- " 13.0867679999999993 0.0000000000000000 0.0000000000000000\n",
- " 0.0000000000000000 13.0867679999999993 0.0000000000000000\n",
- " 0.0000000000000000 0.0000000000000000 13.0867679999999993\n",
- " Cd Te\n",
- " 31 32\n",
- "Direct\n",
- " 0.0014403846070577 0.0152341826280604 0.4960600473735149\n",
- " 0.0018443102488570 0.5161087673464303 -0.0040398656877614\n"
- ]
- }
- ],
- "source": [
- "!head v_Cd_0/groundstate_POSCAR # groundstate structure from -60% distortion relaxation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "id": "5b424741-3831-4c65-81ae-b8a8b2eb20f2",
- "metadata": {},
"outputs": [],
"source": [
- "!diff v_Cd_0/groundstate_POSCAR v_Cd_0/Bond_Distortion_-60.0%/CONTCAR # groundstate structure from -60% distortion relaxation"
+ "!diff v_Cd_s0_0/groundstate_POSCAR v_Cd_s0_0/Bond_Distortion_-60.0%/CONTCAR # groundstate structure from -60% distortion relaxation"
]
},
{
"cell_type": "markdown",
- "id": "9b7e359d-0db6-435b-8385-5ab1151a3f8e",
+ "id": "4555c8af",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2899,7 +2573,7 @@
},
{
"cell_type": "markdown",
- "id": "1ae78fa6-2980-4b11-9511-7a2f96d78ee3",
+ "id": "e3be62eb",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -2911,9 +2585,13 @@
},
{
"cell_type": "code",
- "execution_count": 75,
- "id": "101f20c7-1544-4be5-b20d-a293c618a2d6",
+ "execution_count": null,
+ "id": "a0b6234b",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
@@ -2924,327 +2602,32 @@
"\n",
"# Parse all structures obtained with distortions and unperturbed relaxation. \n",
"# This gives a dictionary matching initial distortion to final structure\n",
- "v_Cd_0 = analysis.get_structures(\"v_Cd_0\")"
+ "v_Cd_s0_0 = analysis.get_structures(\"v_Cd_s0_0\")"
]
},
{
"cell_type": "code",
- "execution_count": 76,
- "id": "678a51a9-098a-4c5d-a5e8-8075775153c8",
+ "execution_count": null,
+ "id": "a42aa14a",
"metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1mv_Cd_0 structural analysis \u001b[0m\n",
- "Analysing site V [0. 0. 0.]\n",
- "Local order parameters (i.e. resemblance to given structural motif, via CrystalNN):\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Coordination | \n",
- " Factor | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " square co-planar | \n",
- " 0.09 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " tetrahedral | \n",
- " 1.00 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " rectangular see-saw-like | \n",
- " 0.01 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " see-saw-like | \n",
- " 0.24 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " trigonal pyramidal | \n",
- " 0.25 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Element | \n",
- " Distance (Å) | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " Te | \n",
- " 2.60 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " Te | \n",
- " 2.63 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " Te | \n",
- " 2.63 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " Te | \n",
- " 2.65 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Coordination | \n",
- " Factor | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " square co-planar | \n",
- " 0.13 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " tetrahedral | \n",
- " 0.74 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " rectangular see-saw-like | \n",
- " 0.03 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " see-saw-like | \n",
- " 0.21 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " trigonal pyramidal | \n",
- " 0.21 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Element | \n",
- " Distance (Å) | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " Te | \n",
- " 2.19 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " Te | \n",
- " 2.63 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " Te | \n",
- " 2.64 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " Te | \n",
- " 2.30 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Bond Distortion | \n",
- " Σ{Displacements} (Å) | \n",
- " Max Distance (Å) | \n",
- " Δ Energy (eV) | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " -0.6 | \n",
- " 5.873 | \n",
- " 0.810 | \n",
- " -0.76 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " -0.5 | \n",
- " 0.000 | \n",
- " 0.024 | \n",
- " -0.01 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " -0.4 | \n",
- " 5.760 | \n",
- " 0.808 | \n",
- " -0.75 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " -0.3 | \n",
- " 5.872 | \n",
- " 0.808 | \n",
- " -0.75 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " -0.2 | \n",
- " 0.000 | \n",
- " 0.025 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 5 | \n",
- " -0.1 | \n",
- " 0.000 | \n",
- " 0.028 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 6 | \n",
- " 0.0 | \n",
- " 0.000 | \n",
- " 0.030 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 7 | \n",
- " 0.1 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 8 | \n",
- " 0.2 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " 0.3 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 10 | \n",
- " 0.4 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 11 | \n",
- " 0.5 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 12 | \n",
- " 0.6 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- " | 13 | \n",
- " 20.0%_from_-1 | \n",
- " 2.285 | \n",
- " 0.237 | \n",
- " -0.28 | \n",
- "
\n",
- " \n",
- " | 14 | \n",
- " Unperturbed | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.00 | \n",
- "
\n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Site | \n",
- " Frac coords | \n",
- " Site mag | \n",
- " Dist. (Å) | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | O(35) | \n",
- " O(35) | \n",
- " [0.0, 0.167, 0.014] | \n",
- " 1.458 | \n",
- " 2.26 | \n",
- "
\n",
- " \n",
- " | O(53) | \n",
- " O(53) | \n",
- " [-0.0, 0.167, 0.486] | \n",
- " 1.478 | \n",
- " 2.26 | \n",
- "
\n",
- " \n",
- " | O(62) | \n",
- " O(62) | \n",
- " [0.165, 0.167, 0.292] | \n",
- " 1.522 | \n",
- " 1.91 | \n",
- "
\n",
- " \n",
- " | O(68) | \n",
- " O(68) | \n",
- " [0.835, 0.167, 0.292] | \n",
- " 1.521 | \n",
- " 1.91 | \n",
- "
\n",
- " \n",
- "
\n",
- "