Update to v0.4.8

hackingmaterials · Dec 19, 2018 · 6a79678 · 6a79678
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diff --git a/docs/_sources/changelog.rst.txt b/docs/_sources/changelog.rst.txt
@@ -5,6 +5,14 @@
 matminer Changelog
 ==================
 
+**v0.4.8**
+
+* CGCNN featurizer / model (Q. Wang, T. Xie)
+* Text-mined element embedding featurizer (A. Dunn)
+* add Brgoch data set (D. Dopp)
+* add quartile to PropertyStats (A. Dunn)
+* minor fixes, improvements (A. Dunn, A. Jain)
+
 **v0.4.6**
 
 * Jarvis CFID descriptors (A. Dunn, K. Choudhary)

diff --git a/docs/_sources/contributors.rst.txt b/docs/_sources/contributors.rst.txt
@@ -52,4 +52,5 @@ Other
 * David Waroquiers (UC Louvain, Belgium)
 * Logan Williams (U Michigan Ann Arbor)
 * Aidan Thompson (Sandia National Lab)
-* Kamal Choudhary (NIST)
+* Kamal Choudhary (NIST)
+* Tian Xie (MIT)
diff --git a/docs/_sources/dataset_summary.rst.txt b/docs/_sources/dataset_summary.rst.txt
@@ -70,6 +70,60 @@ publisher = {Dryad Digital Repository}
 
 
 
+-------------------------
+brgoch_superhard_training
+-------------------------
+2574 materials used for training regressors that predict shear and bulk modulus.
+
+**Number of entries:** 2574
+
+============= ============================================================================================================================================================
+Column        Description
+============= ============================================================================================================================================================
+brgoch_feats  features used in brgoch study compressed to a dictionary
+bulk_modulus  VRH bulk modulus
+composition   pymatgen composition object
+formula       Chemical formula as a string
+material_id   materials project id
+structure     pymatgen structure object
+shear_modulus VRH shear modulus
+suspect_value True if bulk or shear value did not closely match (within 5%/1GPa of MP) materials project value at time of cross reference or if no material could be found
+============= ============================================================================================================================================================
+
+
+
+**Reference**
+
+Machine Learning Directed Search for Ultraincompressible, Superhard Materials
+Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch
+Journal of the American Chemical Society 2018 140 (31), 9844-9853
+DOI: 10.1021/jacs.8b02717
+
+
+
+**Bibtex Formatted Citations**
+
+@article{doi:10.1021/jacs.8b02717,
+author = {Mansouri Tehrani, Aria and Oliynyk, Anton O. and Parry, Marcus and Rizvi, Zeshan and Couper, Samantha and Lin, Feng and Miyagi, Lowell and Sparks, Taylor D. and Brgoch, Jakoah},
+title = {Machine Learning Directed Search for Ultraincompressible, Superhard Materials},
+journal = {Journal of the American Chemical Society},
+volume = {140},
+number = {31},
+pages = {9844-9853},
+year = {2018},
+doi = {10.1021/jacs.8b02717},
+note ={PMID: 30010335},
+URL = {
+https://doi.org/10.1021/jacs.8b02717
+},
+eprint = {
+https://doi.org/10.1021/jacs.8b02717
+}
+}
+
+
+
+
 --------------------
 castelli_perovskites
 --------------------
@@ -622,6 +676,48 @@ https://doi.org/10.1021/acs.jpclett.7b01046
 
 
 
+---------------
+glass_binary_v2
+---------------
+Identical to glass_binary dataset, but with duplicate entries merged. If there was a disagreement in gfa when merging the class was defaulted to 1.
+
+**Number of entries:** 5483
+
+======= =================================================================================================================================================================================
+Column  Description
+======= =================================================================================================================================================================================
+formula chemical formula
+gfa     glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming ("AM"), 0: non-full-forming("CR")
+======= =================================================================================================================================================================================
+
+
+
+**Reference**
+
+https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046
+
+
+
+**Bibtex Formatted Citations**
+
+@article{doi:10.1021/acs.jpclett.7b01046,
+author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.},
+title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability},
+journal = {The Journal of Physical Chemistry Letters},
+volume = {8},
+number = {14},
+pages = {3434-3439},
+year = {2017},
+doi = {10.1021/acs.jpclett.7b01046},
+note ={PMID: 28697303},
+eprint = {
+https://doi.org/10.1021/acs.jpclett.7b01046
+
+}}
+
+
+
+
 ------------------
 glass_ternary_hipt
 ------------------

diff --git a/docs/_sources/featurizer_summary.rst.txt b/docs/_sources/featurizer_summary.rst.txt
@@ -171,6 +171,7 @@ Name                                           Description
 :code:`ChemicalOrdering`                       How much the ordering of species in the structure differs from random `[more] <https://hackingmaterials.github.io/matminer/matminer.featurizers.html#matminer.featurizers.structure.ChemicalOrdering>`_
 :code:`StructureComposition`                   Features related to the composition of a structure `[more] <https://hackingmaterials.github.io/matminer/matminer.featurizers.html#matminer.featurizers.structure.StructureComposition>`_
 :code:`XRDPowderPattern`                       1D array representing powder diffraction of a structure as calculated by `[more] <https://hackingmaterials.github.io/matminer/matminer.featurizers.html#matminer.featurizers.structure.XRDPowderPattern>`_
+:code:`CGCNNFeaturizer`                        Features generated by training a Crystal Graph Convolutional Neural Network `[more] <https://hackingmaterials.github.io/matminer/matminer.featurizers.html#matminer.featurizers.structure.CGCNNFeaturizer>`_
 :code:`JarvisCFID`                             Classical Force-Field Inspired Descriptors (CFID) from Jarvis-ML. `[more] <https://hackingmaterials.github.io/matminer/matminer.featurizers.html#matminer.featurizers.structure.JarvisCFID>`_
 ============================================   ================================================================================================================================================================================================================================================================================================================================================================================================================
 

diff --git a/docs/_sources/matminer.featurizers.utils.rst.txt b/docs/_sources/matminer.featurizers.utils.rst.txt
@@ -4,6 +4,14 @@ matminer.featurizers.utils package
 Submodules
 ----------
 
+matminer.featurizers.utils.cgcnn module
+---------------------------------------
+
+.. automodule:: matminer.featurizers.utils.cgcnn
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 matminer.featurizers.utils.grdf module
 --------------------------------------
 

diff --git a/docs/_static/basic.css b/docs/_static/basic.css
@@ -81,6 +81,10 @@ div.sphinxsidebar input {
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+div.sphinxsidebar #searchbox form.search {
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@@ -427,6 +431,13 @@ table.field-list td, table.field-list th {
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