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

Nature communications, 2020-12, Vol.11 (1), p.6280-6280, Article 6280 [Peer Reviewed Journal]

The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-19964-7 ;PMID: 33293567

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  • Title:
    Predicting materials properties without crystal structure: deep representation learning from stoichiometry
  • Author: Goodall, Rhys E. A. ; Lee, Alpha A.
  • Subjects: Composition ; Computer applications ; Computing costs ; Crystal structure ; Inorganic materials ; Learning algorithms ; Machine learning ; Material properties ; Representations ; Stoichiometry
  • Is Part Of: Nature communications, 2020-12, Vol.11 (1), p.6280-6280, Article 6280
  • Description: 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.
  • Publisher: London: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2041-1723
    EISSN: 2041-1723
    DOI: 10.1038/s41467-020-19964-7
    PMID: 33293567
  • Source: PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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