Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Abstract
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 104 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
- Authors:
-
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science and Engineering
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1524040
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review Letters
- Additional Journal Information:
- Journal Volume: 120; Journal Issue: 14; Journal ID: ISSN 0031-9007
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Xie, Tian, and Grossman, Jeffrey C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. United States: N. p., 2018.
Web. doi:10.1103/PhysRevLett.120.145301.
Xie, Tian, & Grossman, Jeffrey C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. United States. https://doi.org/10.1103/PhysRevLett.120.145301
Xie, Tian, and Grossman, Jeffrey C. Fri .
"Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties". United States. https://doi.org/10.1103/PhysRevLett.120.145301. https://www.osti.gov/servlets/purl/1524040.
@article{osti_1524040,
title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties},
author = {Xie, Tian and Grossman, Jeffrey C.},
abstractNote = {The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 104 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.},
doi = {10.1103/PhysRevLett.120.145301},
journal = {Physical Review Letters},
number = 14,
volume = 120,
place = {United States},
year = {Fri Apr 06 00:00:00 EDT 2018},
month = {Fri Apr 06 00:00:00 EDT 2018}
}
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Works referenced in this record:
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Accelerated search for materials with targeted properties by adaptive design
journal, April 2016
- Xue, Dezhen; Balachandran, Prasanna V.; Hogden, John
- Nature Communications, Vol. 7, Issue 1
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015
- Kirklin, Scott; Saal, James E.; Meredig, Bryce
- npj Computational Materials, Vol. 1, Issue 1
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
journal, October 2016
- de Jong, Maarten; Chen, Wei; Notestine, Randy
- Scientific Reports, Vol. 6, Issue 1
Voronoi–dirichlet polyhedra in crystal chemistry: theory and applications
journal, October 2004
- Blatov *, Vladislav A.
- Crystallography Reviews, Vol. 10, Issue 4
Charting the complete elastic properties of inorganic crystalline compounds
journal, March 2015
- de Jong, Maarten; Chen, Wei; Angsten, Thomas
- Scientific Data, Vol. 2, Issue 1
An Interpretation of Bond Lengths and a Classification of Bonds
journal, December 1951
- Sanderson, R. T.
- Science, Vol. 114, Issue 2973
An Explanation of Chemical Variations within Periodic Major Groups
journal, October 1952
- Sanderson, R. T.
- Journal of the American Chemical Society, Vol. 74, Issue 19
Crystal structure representations for machine learning models of formation energies
journal, April 2015
- Faber, Felix; Lindmaa, Alexander; von Lilienfeld, O. Anatole
- International Journal of Quantum Chemistry, Vol. 115, Issue 16
Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
journal, November 2015
- Seko, Atsuto; Togo, Atsushi; Hayashi, Hiroyuki
- Physical Review Letters, Vol. 115, Issue 20
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013
- Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
- APL Materials, Vol. 1, Issue 1
Improved Photocatalytic Performance under Solar Light Irradiation by Integrating Wide-band-gap Semiconductors, SnO2, SnTaO3 and Sn2Ta2O7
journal, January 2016
- Lang, Junyu; wang, Shuwei; Li, Chongyan
- Materials Today: Proceedings, Vol. 3, Issue 2
Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies
journal, October 1986
- Hybertsen, Mark S.; Louie, Steven G.
- Physical Review B, Vol. 34, Issue 8, p. 5390-5413
Representation of compounds for machine-learning prediction of physical properties
journal, April 2017
- Seko, Atsuto; Hayashi, Hiroyuki; Nakayama, Keita
- Physical Review B, Vol. 95, Issue 14
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
journal, May 2014
- Schütt, K. T.; Glawe, H.; Brockherde, F.
- Physical Review B, Vol. 89, Issue 20
Computational screening of perovskite metal oxides for optimal solar light capture
journal, January 2012
- Castelli, Ivano E.; Olsen, Thomas; Datta, Soumendu
- Energy Environ. Sci., Vol. 5, Issue 2
Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015
- Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V.
- Physical Review Letters, Vol. 114, Issue 10
A high-throughput infrastructure for density functional theory calculations
journal, June 2011
- Jain, Anubhav; Hautier, Geoffroy; Moore, Charles J.
- Computational Materials Science, Vol. 50, Issue 8
Machine Learning Energies of 2 Million Elpasolite Crystals
journal, September 2016
- Faber, Felix A.; Lindmaa, Alexander; von Lilienfeld, O. Anatole
- Physical Review Letters, Vol. 117, Issue 13
The Inorganic Crystal Structure Database (ICSD)—Present and Future
journal, January 2004
- Hellenbrandt, Mariette
- Crystallography Reviews, Vol. 10, Issue 1
Universal fragment descriptors for predicting properties of inorganic crystals
journal, June 2017
- Isayev, Olexandr; Oses, Corey; Toher, Cormac
- Nature Communications, Vol. 8, Issue 1
Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
journal, January 2015
- Isayev, Olexandr; Fourches, Denis; Muratov, Eugene N.
- Chemistry of Materials, Vol. 27, Issue 3
The thermodynamic scale of inorganic crystalline metastability
journal, November 2016
- Sun, Wenhao; Dacek, Stephen T.; Ong, Shyue Ping
- Science Advances, Vol. 2, Issue 11
Covalent radii revisited
journal, January 2008
- Cordero, Beatriz; Gómez, Verónica; Platero-Prats, Ana E.
- Dalton Transactions, Issue 21
Cubic lead perovskite with anomalous metallic behavior
journal, April 2017
- Takatsu, Hiroshi; Hernandez, Olivier; Yoshimune, Wataru
- Physical Review B, Vol. 95, Issue 15
Phase Transitions in Solid Solutions of PbZrO 3 and PbTiO 3 (II) X-ray Study
journal, January 1952
- Shirane, Gen; Suzuki, Kazuo; Takeda, Akitsu
- Journal of the Physical Society of Japan, Vol. 7, Issue 1
Universal fragment descriptors for predicting properties of inorganic crystals
text, January 2017
- Olexandr, Isayev,; Corey, Oses,; Eric, Gossett,
- The University of North Carolina at Chapel Hill University Libraries
Crystal structure representations for machine learning models of formation energies
text, January 2015
- Felix, Faber,; Alexander, Lindmaa,; Anatole, von Lilienfeld, O.
- Wiley
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
text, January 2015
- Kirklin, Scott; Saal, James E.; Meredig, Bryce
- London : Nature Publ. Group
Big Data of Materials Science - Critical Role of the Descriptor
text, January 2014
- Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V.
- arXiv
Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
text, January 2014
- Isayev, Olexandr; Fourches, Denis; Muratov, Eugene N.
- arXiv
Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
text, January 2015
- Seko, Atsuto; Togo, Atsushi; Hayashi, Hiroyuki
- arXiv
Representation of compounds for machine-learning prediction of physical properties
text, January 2016
- Seko, Atsuto; Hayashi, Hiroyuki; Nakayama, Keita
- arXiv
Works referencing / citing this record:
Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning
journal, January 2019
- Li, Zhenzhu; Xu, Qichen; Sun, Qingde
- Advanced Functional Materials, Vol. 29, Issue 9
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy
journal, July 2019
- Ma, Wei; Cheng, Feng; Xu, Yihao
- Advanced Materials, Vol. 31, Issue 35
Predicting Thermal Properties of Crystals Using Machine Learning
journal, December 2019
- Tawfik, Sherif Abdulkader; Isayev, Olexandr; Spencer, Michelle J. S.
- Advanced Theory and Simulations, Vol. 3, Issue 2
Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, September 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 6, Issue 21
A Critical Review of Machine Learning of Energy Materials
journal, January 2020
- Chen, Chi; Zuo, Yunxing; Ye, Weike
- Advanced Energy Materials, Vol. 10, Issue 8
Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
journal, July 2019
- Olsthoorn, Bart; Geilhufe, R. Matthias; Borysov, Stanislav S.
- Advanced Quantum Technologies, Vol. 2, Issue 7-8
Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
journal, August 2018
- Dimiduk, Dennis M.; Holm, Elizabeth A.; Niezgoda, Stephen R.
- Integrating Materials and Manufacturing Innovation, Vol. 7, Issue 3
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
journal, June 2019
- Xie, Tian; France-Lanord, Arthur; Wang, Yanming
- Nature Communications, Vol. 10, Issue 1
Recent advances and applications of machine learning in solid-state materials science
journal, August 2019
- Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
- npj Computational Materials, Vol. 5, Issue 1
Graph similarity drives zeolite diffusionless transformations and intergrowth
journal, October 2019
- Schwalbe-Koda, Daniel; Jensen, Zach; Olivetti, Elsa
- Nature Materials, Vol. 18, Issue 11
A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
journal, September 2019
- Mailoa, Jonathan P.; Kornbluth, Mordechai; Batzner, Simon
- Nature Machine Intelligence, Vol. 1, Issue 10
Machine learning material properties from the periodic table using convolutional neural networks
journal, January 2018
- Zheng, Xiaolong; Zheng, Peng; Zhang, Rui-Zhi
- Chemical Science, Vol. 9, Issue 44
Extensive deep neural networks for transferring small scale learning to large scale systems
journal, January 2019
- Mills, Kyle; Ryczko, Kevin; Luchak, Iryna
- Chemical Science, Vol. 10, Issue 15
New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more
journal, May 2019
- Urban, Jeffrey J.; Menon, Akanksha K.; Tian, Zhiting
- Journal of Applied Physics, Vol. 125, Issue 18
Machine learning for interatomic potential models
journal, February 2020
- Mueller, Tim; Hernandez, Alberto; Wang, Chuhong
- The Journal of Chemical Physics, Vol. 152, Issue 5
Predicting HSE band gaps from PBE charge densities via neural network functionals
journal, January 2020
- Lentz, Levi C.; Kolpak, Alexie M.
- Journal of Physics: Condensed Matter, Vol. 32, Issue 15
From DFT to machine learning: recent approaches to materials science–a review
journal, May 2019
- Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera
- Journal of Physics: Materials, Vol. 2, Issue 3
New tolerance factor to predict the stability of perovskite oxides and halides
journal, February 2019
- Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R.
- Science Advances, Vol. 5, Issue 2
Inverse design of porous materials using artificial neural networks
journal, January 2020
- Kim, Baekjun; Lee, Sangwon; Kim, Jihan
- Science Advances, Vol. 6, Issue 1
Crystal symmetry determination in electron diffraction using machine learning
journal, January 2020
- Kaufmann, Kevin; Zhu, Chaoyi; Rosengarten, Alexander S.
- Science, Vol. 367, Issue 6477
Deep materials informatics: Applications of deep learning in materials science
journal, June 2019
- Agrawal, Ankit; Choudhary, Alok
- MRS Communications, Vol. 9, Issue 3
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
journal, July 2019
- Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva
- MRS Communications, Vol. 9, Issue 3
Convolutional Neural Networks for Crystal Material
Property Prediction Using Hybrid Orbital-Field
Matrix and Magpie Descriptors
journal, April 2019
- Cao, Zhuo; Dan, Yabo; Xiong, Zheng
- Crystals, Vol. 9, Issue 4
Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, November 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 7, Issue 2
Extensive deep neural networks for transferring small scale learning to large scale systems
text, January 2017
- Mills, Kyle; Ryczko, Kevin; Luchak, Iryna
- arXiv
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
text, January 2018
- Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R.
- arXiv
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
text, January 2018
- Choudhary, Kamal; DeCost, Brian; Tavazza, Francesca
- arXiv
Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy
preprint, January 2019
- Ma, Wei; Cheng, Feng; Xu, Yihao
- arXiv
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
text, January 2019
- Xie, Tian; France-Lanord, Arthur; Wang, Yanming
- arXiv
Data-driven materials science: status, challenges and perspectives
text, January 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam S.
- arXiv
Deep Learning for Automated Classification and Characterization of Amorphous Materials
preprint, January 2019
- Swanson, Kirk; Trivedi, Shubhendu; Lequieu, Joshua
- arXiv