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Title: 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:
 [1];  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science and Engineering
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (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. doi: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. doi: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 = {2018},
month = {4}
}

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