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Title: Hierarchical visualization of materials space with graph convolutional neural networks

Abstract

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property spaces. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materialsmore » space in automated materials design.« less

Authors:
 [1];  [1]
+ Show Author Affiliations
  1. 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); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1543881
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 149; Journal Issue: 17; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; Physics

Citation Formats

Xie, Tian, and Grossman, Jeffrey C. Hierarchical visualization of materials space with graph convolutional neural networks. United States: N. p., 2018. Web. doi:10.1063/1.5047803.
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Xie, Tian, & Grossman, Jeffrey C. Hierarchical visualization of materials space with graph convolutional neural networks. United States. https://doi.org/10.1063/1.5047803
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Xie, Tian, and Grossman, Jeffrey C. Tue . "Hierarchical visualization of materials space with graph convolutional neural networks". United States. https://doi.org/10.1063/1.5047803. https://www.osti.gov/servlets/purl/1543881.
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@article{osti_1543881,
title = {Hierarchical visualization of materials space with graph convolutional neural networks},
author = {Xie, Tian and Grossman, Jeffrey C.},
abstractNote = {The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property spaces. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design.},
doi = {10.1063/1.5047803},
journal = {Journal of Chemical Physics},
number = 17,
volume = 149,
place = {United States},
year = {Tue Nov 06 00:00:00 EST 2018},
month = {Tue Nov 06 00:00:00 EST 2018}
}
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Journal Article:
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https://doi.org/10.1063/1.5047803
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Cited by: 41 works
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Figures / Tables:

FIG. 1 FIG. 1: The structure of the crystal graph convolutional neural networks.
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