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Hierarchical visualization of materials space with graph convolutional neural networks

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/1.5047803· OSTI ID:1543881
 [1];  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science and Engineering
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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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1543881
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 17 Vol. 149; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Cited By (8)

A Critical Review of Machine Learning of Energy Materials journal January 2020
Making machine learning a useful tool in the accelerated discovery of transition metal complexes journal July 2019
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials journal June 2019
Recent advances and applications of machine learning in solid-state materials science journal August 2019
Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity journal January 2020
A quantitative uncertainty metric controls error in neural network-driven chemical discovery journal January 2019
Predicting charge density distribution of materials using a local-environment-based graph convolutional network journal November 2019
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials text January 2019

Figures / Tables (7)

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