13.08.2020 Andrij Vasylenko
This submission is a part of the research
A. Vasylenko et al. "Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry", Nat. Commun. 12, 1-12 (2021).
When using this code or any of the numerical results in this repository, please cite the above paper approriately.
In materials science, selection of 118 chemical elements to combine ultimately determines whether a elemental combination (called phase field) can form a stable compound. The combinatorial challenge is overwhelming, e.g., there are 118! / (3! * 115!) = 266916 possible ternary phase fields, with only a fraction of them explored synthetically. While scientists with accumulated knowledge of chemistry of materials can argue, that many combinations can afford stable compounds, depending on the particular stoichiometric ratios, it is still impossible to prioritise the likely attractive combinations.
This model (PhaseRank) affords numerical assesment of the phase fields based on the knowledge exctracted with unsupervised learning of the Inorganic Crystal Structure Database (ICSD) of materials, and ranks the unexplored chemistry by similarity with the materials found in ICSD.
python-3.8
pip (version 19.0 or later)
OS:
Ubuntu (version 18.04 or later)
MacOS (Catalina 10.15.6 or later)
TensorFlow (version 2.0 or later)
PyOD (0.7.8 or later)
scikit-learn-0.20.0
Dependencies can be installed automatically during installation.
pip install rankfields
rankfields <input_file>
E.g., rankfields rpp.input
should reproduce results for Supplementary Table S3.
The results for this and other example runs with inputs from ranking_phase_fields/test_input/
are copied in ranking_phase_fields/test_results/
Unsupervised pattern recognition methods utilise PyOD library.
Models:
'AE' : AutoEncoder
'VAE' : Variational AE
'ABOD' : Angle-based outlier detection
'FeatureBagging' : FeatureBagging
'HBOS' : Histogram-Based Outlier Score
'IForest' : Isolation Forest
'KNN' : K-Nearest Neighbours
'LOF' : Local Outlier Factor
'OCSVM' : OCSupport Vector Machine
'PCA' : Principle Component Analysis
'SOS' : SOS(),
'COF' : Connectivity-based OF(),
'CBLOF' : Clustering-Based LOF
'SOD' : SOD(),
'LOCI' : LOCI(),
'MCD' : MCD()
Read more about these methods https://www.pyod.readthedocs.io
[1] Zhao, Y., Nasrullah, Z. and Li, Z., PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research 20(96), pp.1-7 (2019).
Supported elemental features:
TABLES/AtomicVolume.table
TABLES/AtomicWeight.table
TABLES/BCCbandgap.table
TABLES/BCCefflatcnt.table
TABLES/BCCenergy_pa.table
TABLES/BCCenergydiff.table
TABLES/BCCfermi.table
TABLES/BCCmagmom.table
TABLES/BCCvolume_pa.table
TABLES/BCCvolume_padiff.table
TABLES/BoilingT.table
TABLES/Column.table
TABLES/CovalentRadius.table
TABLES/Density.table
TABLES/Electronegativity.table
TABLES/FirstIonizationEnergy.table
TABLES/GSbandgap.table
TABLES/GSefflatcnt.table
TABLES/GSenergy_pa.table
TABLES/GSestBCClatcnt.table
TABLES/GSestFCClatcnt.table
TABLES/GSmagmom.table
TABLES/GSvolume_pa.table
TABLES/ICSDVolume.table
TABLES/MeltingT.table
TABLES/MendeleevNumber.table
TABLES/MiracleRadius.table
TABLES/NUnfilled.table
TABLES/NValance.table
TABLES/NdUnfilled.table
TABLES/NdValence.table
TABLES/NfUnfilled.table
TABLES/NfValence.table
TABLES/NpUnfilled.table
TABLES/NpValence.table
TABLES/NsUnfilled.table
TABLES/NsValence.table
TABLES/Number.table
TABLES/Pettifor.table
TABLES/Polarizability.table
TABLES/Row.table
[2] Jha, D., Ward, L., Paul, A. et al. ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition. Sci Rep 8, 17593 (2018). https://doi.org/10.1038/s41598-018-35934-y
[3] Glawe, H., Sanna, A., Gross, E. K. U., Marques, M. A. L., The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining. N. J. Phys. 18, 093011 (2016). https://doi.org/10.1088/1367-2630/18/9/093011
(default values are in the rpp.input file)
| parameter | value |
|---|---|
| icsd_file | (default: icsd2017) ICSD excerpt. A text file, a list of ICSD .cif files with specified oxidation states for each element. |
| phase_fields | (default: quaternary) binary, ternary, quaternary - are supported. Type of phase fields to investigate. |
| cations_train | (default: all) list of elements constituting a phase field in ICSD. Elements in the first positions (cations), e.g. elements for M and M' in MM'AA' phase fields. |
| anions_train | (default: S,O,Cl,Br,F,N,Te,P,Se,As,I) list of elements constituting a phase field in ICSD. Elements in the last positions (anions) e.g. elements for A and A' in MM'AA' phase fields. |
| nanions_train | (default: 2) Number of anions (elements with negative oxidation states as specified in icsd_file) in the training set. Supported values: 0, 1, 2. If 0 - oxidation states are not taken into account. |
| cation1_test | (default: Li) list of elements for the first position (e.g. M in MM'AA' phase fields) in the phase fields to explore (no reported associated compositions in ICSD). |
| cation2_test | (default: all) list of elements for the second position (e.g. M' in MM'AA' phase fields) in the phase fields to explore (no reported associated compositions in ICSD). Ignored for binary and ternary (type MAA') phase fields. |
| anion1_test | (default: S,O,Cl,Br,I,F,N) list of elements for the 3rd position (e.g. A in MM'AA' phase fields) in the phase fields to explore (no reported associated compositions in ICSD). Stands for a 3rd cation in ternary (type MM'M") and quaternary (type MM'M"A) phase fields. |
| anion2_test | (default: S,O,Cl,Br,I,F,N) list of elements for the 4th position (e.g. A' in MM'AA' phase fields) in the phase fields to explore (no reported associated compositions in ICSD). Ignored for ternary (type MM'A) phase fields. |
| method | (default: VAE) See all supported models above. |
| cross-validate | (default: Fault) If True sets 5-fold cross-validation of the model. |
| average_runs | (default: 1) Number of runs to average the scores over. Makes sense for not neural network based (AE, VAE) methods. |
| features | (default: See rpp.input). See all supported features above. |