IMPORTANT: If you create a new version of this repository, please remember to update the link to it in the MALA CPU test workflow! Instructions on how to do this are provided in the respective workflow yaml file cpu-tests.yml.
This repository contains data to test, develop and debug MALA and MALA based runscripts. If you plan to do machine-learning tests ("Does this network implementation work? Is this new data loading strategy working?"), this is the right data to test with. It is NOT production level data!
Contains DFT calculation output from a QuantumEspresso calculation for a beryllium cell with 2 atoms, along with input scripts and pseudopotential to replicate this calculation. LDOS files are usually large, therefore this reduced example samples the LDOS somewhat inaccurately, in order to reduce storage size. The energy grid for the LDOS is 11 entries long, starting at -5 eV with a spacing of 2.5 eV. For LDOS and descriptors, 4 snapshots are contained. In detail, the following data files can be found:
| File Name | Description |
|---|---|
recreate_data/ |
Input scripts for QE |
cubes/ |
.cube files for the local density of states |
Be.pbe-n-rrkjus_psl.1.0.0.UPF |
Pseudopotential used for the QE calculation |
Be_snapshot0.dens.npy |
Electronic density numpy array (snapshot 0) |
Be_snapshot.dens.h5 |
Electronic density (HDF5 format, see details below) |
Be_snapshot0.dos.npy |
Density of states numpy array (snapshot 0) |
Be_snapshot0-3.out |
Output file of QE. calculation |
Be_snapshot0-3.in.npy |
Bispectrum descriptors numpy array |
Be_snapshot0-3.out.npy |
Local density of states numpy array |
Be_snapshot0-3.in.h5 |
Bispectrum descriptors (HDF5 format) |
Be_snapshot0-3.out.h5 |
Local density of states (HDF5 format) |
Be_model.zip |
MALA trained model archive for examples and tests |
SNAP bispectrum descriptors of length 91 on 18 x 18 x 27 real space grid.
Note
In the last dimension of length 94, the first 3 entries are the grid coordinates / indices (an artifact of the SNAP vector generation). The actual features are snap_array[..., 3:].
>>> np.load('Be2/Be_snapshot1.in.npy').shape
(18, 18, 27, 94)LDOS (11 points) on 18 x 18 x 27 real space grid.
>>> np.load('Be2/Be_snapshot1.out.npy').shape
(18, 18, 27, 11)Density of states (only provided for snapshot 0):
>>> np.load('Be2/Be_snapshot0.dos.npy').shape
(11,)Density for snapshot 0 on a 18 x 18 x 27 real space grid. The extra dimension
can be ignored, i.e. use d=np.load(...); d[..., -1] to squeeze the shape to
(18, 18, 27).
>>> np.load('Be2/Be_snapshot0.dens.npy').shape
(18, 18, 27, 1)MALA supports the openPMD format, so we also provide data in that format here.
$ h5ls -r Be_snapshot0.in.h5 | grep Dataset | sort -V
/data/0/meshes/Bispectrum/0 Dataset {18, 18, 27}
/data/0/meshes/Bispectrum/1 Dataset {18, 18, 27}
...
/data/0/meshes/Bispectrum/93 Dataset {18, 18, 27}
$ h5ls -r Be_snapshot0.out.h5 | grep Dataset | sort -V
/data/0/meshes/LDOS/0 Dataset {18, 18, 27}
/data/0/meshes/LDOS/1 Dataset {18, 18, 27}
...
/data/0/meshes/LDOS/10 Dataset {18, 18, 27}For the density, the snapshot number 0 is encoded in the name
/data/0.
$ h5ls -r Be_snapshot.dens.h5 | grep Dataset
/data/0/meshes/Density/0 Dataset {18, 18, 27}To understand the naming scheme, we can use openPMD's introspection tool:
$ openpmd-ls Be_snapshot.dens.h5
openPMD series: Be_snapshot.dens
openPMD standard: 1.1.0
openPMD extensions: 0
data author: ...
data created: 2023-05-23 15:37:18 +0200
data backend: HDF5
generating machine: unknown
generating software: MALA (version: 1.1.0)
generating software dependencies: unknown
number of iterations: 1 (groupBased)
all iterations: 0
number of meshes: 1
all meshes:
Density
number of particle species: 0
So /data/0/ is the openPMD iteration counter, which we use to name snapshots.
Density/0 is one grid / array / Dataset (in hdf terms) / mesh (in openPMD
terms) of shape 18 x 18 x 27. Multiple snapshots in one file would be called
/data/0/meshes/Density/0 Dataset {18, 18, 27}
/data/1/meshes/Density/0 Dataset {18, 18, 27}
/data/2/meshes/Density/0 Dataset {18, 18, 27}
...