cgcnn—a graph representation of materials for property...
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CGCNN—A Graph Representation of Materials for Property Prediction and
Materials Design
Tian Xie, Jeffrey GrossmanDepartment of Materials Science and Engineering,
Massachusetts Institute of Technology11/29/2018
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Deep learning for different data types
Grossman’s Group, Massachusetts Institute of Technology
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Images
Grid-like
Language
Time series
Graph
Relational
CNNTranslation invariance
RNNTime
invariance
GCNNode
invarianceBattaglia, et al., arXiv preprint arXiv:1806.01261 (2018).
Invariances are built into the neural networks to prevent overfitting
Representation of solid materials
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Molecule graphs• Node invariance à atom
indistinguishability • Graph construction is straightforward• All information encoded
Gasteiger, et al. Angew. Chem. Int. Ed. Engl. 32.4 (1993): 503-527.Duvenaud, et al. Adv Neural Inf Process Syst. 2015.Kearnes, et al. J. Comput. Aided Mol. Des. 30.8 (2016): 595-608.Gilmer, et al. arXiv preprint arXiv:1704.01212 (2017).Crystal (solid material) graphs
• 3D structure of materials are important
• Many materials are periodic• Bond determination can be
ambiguous• Rare elements
Innovation needed for graph construction and network design
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Design of CGCNN
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Tian Xie, and Jeffrey C. Grossman. Phys. Rev. Lett. 120.14 (2018): 145301.
Crystal Graph Convolutional Neural Networks (CGCNN)
¡ 3D structure: use distances as edge attributes
¡ Periodicity: multigraphs instead of normal graphs
¡ Rare elements: element encoding based on elemental properties
¡ Bond ambiguity: ”gated architecture” in GCN
3D molecules:Schütt, et al. Nat. Commun. 8 (2017): 13890.Schütt, et al. J. Chem. Phys. 148.24 (2018): 241722.
Na1 Na1Cl1
Periodic image
Prediction performance
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¡ Materials project: 87 elements, 216 space groups, up to 7 different elements and 200 atoms in unit cell.
¡ Achieve DFT accuracy on test sets for 7 properties with 10# data points.
Tian Xie, and Jeffrey C. Grossman. Phys. Rev. Lett. 120.14 (2018): 145301.
Application: solid state electrolytes
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Zeeshan Ahmad, Tian Xie, et al. ACS Cent. Sci. 4.8 (2018): 996-1006.
¡ Lithium metal batteries¡ Dendrite growth at the interface
between Lithium metal and electrolytes is a big problem
¡ Causes short circuit and capacity loss¡ Design new electrolytes to suppress
dendrite growth
¡ Kinetic model for the stability of the interface¡ Inputs: shear/bulk modulus, molar
volumes¡ Output: Stability parameter !
(negative means stable)
Ahmad, et al. Phys. Rev. Lett. 119.5 (2017): 056003.In collaboration with Zeeshan & Prof. Viswanathan from CMU
Application: solid state electrolytes
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In collaboration with Zeeshan & Prof. Viswanathan from CMU
¡ Screening Li-containing solids¡ Data: ~3400 training data from Materials
Project¡ Predicted stability parameter ! for
12,950 solid materials¡ Identified several candidates
¡ Uncertainty estimation¡ CGCNN ensembles for uncertainty
estimation¡ Extrapolation can cause higher
uncertainties
Zeeshan Ahmad, Tian Xie, et al. ACS Cent. Sci. 4.8 (2018): 996-1006.
Hierarchical visualization of CGCNN
Grossman’s Group, Massachusetts Institute of Technology
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¡ Elemental representation¡ Only the information of
elements
¡ Local representation¡ Surrounding elements¡ Local configuration
Tian Xie, and Jeffrey C. Grossman. J. Chem. Phys. 149.17 (2018): 174111.
¡ Local energy¡ Energy of the atom in
local environments
Perovskites: different compositions
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¡ Perovskites¡ A crystal structure
type ABC3
¡ Site A, B: metals¡ Site C: O, F, S, N
Tian Xie, and Jeffrey C. Grossman. J. Chem. Phys. 149.17 (2018): 174111.
Less data needed for element representations
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Tian Xie, and Jeffrey C. Grossman. J. Chem. Phys. 149.17 (2018): 174111.
234 training data (1/64)
937 training data (1/16)
3,750 training data (1/4)
15,000 training data
Boron: different configurations
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Tian Xie, and Jeffrey C. Grossman. J. Chem. Phys. 149.17 (2018): 174111.
Energy of different coordination environments
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Tian Xie, and Jeffrey C. Grossman. J. Chem. Phys. 149.17 (2018): 174111.
Tm-H solid solutions
Special stability due to Al-O, Si-O, P-O, S-O covalent bond
¡ Local energy of all 734,077 distinct coordination environments in Materials Project
¡ General trends¡ Diagonal lines: zero
local energy¡ Most stable: high
valence metals + high electronegativity non-metals à strong ionic bondseV/atom
Summary
¡ CGCNN provides a general framework to learn structure-property relations for solid materials.
¡ Uncertainty estimation is important when applying ML to real material systems.
¡ Visualization of neural networks can provide additional insights for materials design.
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References and codes
¡ References:¡ Xie, Tian, and Jeffrey C. Grossman. "Crystal Graph Convolutional Neural
Networks for an Accurate and Interpretable Prediction of Material Properties." Physical review letters 120.14 (2018): 145301.
¡ Ahmad, Zeeshan, et al. "Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes." ACS central science 4.8 (2018): 996-1006.
¡ Xie, Tian, and Jeffrey C. Grossman. "Hierarchical visualization of materials space with graph convolutional neural networks." The Journal of chemical physics 149.17 (2018): 174111.
¡ Codes:¡ CGCNN: https://github.com/txie-93/cgcnn
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Thank you!