Predicting materials properties without crystal structure: deep representation learning from stoichiometry

Rhys E. A. Goodall and Alpha A. Lee ()
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Rhys E. A. Goodall: University of Cambridge, Cavendish Laboratory
Alpha A. Lee: University of Cambridge, Cavendish Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure — therefore only applicable to materials with already characterised structures — or structure-agnostic fixed-length representations hand-engineered from the stoichiometry. We develop a machine learning approach that takes only the stoichiometry as input and automatically learns appropriate and systematically improvable descriptors from data. Our key insight is to treat the stoichiometric formula as a dense weighted graph between elements. Compared to the state of the art for structure-agnostic methods, our approach achieves lower errors with less data.

Date: 2020
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DOI: 10.1038/s41467-020-19964-7

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