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

      research-article
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      Nature Communications
      Nature Publishing Group UK
      Structure prediction, Materials science, Computational methods

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          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.

          Abstract

          Predicting the structure of unknown materials’ compositions represents a challenge for high-throughput computational approaches. Here the authors introduce a new stoichiometry-based machine learning approach for predicting the properties of inorganic materials from their elemental compositions.

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            Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

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              SchNet – A deep learning architecture for molecules and materials

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                Author and article information

                Contributors
                aal44@cam.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 December 2020
                8 December 2020
                2020
                : 11
                : 6280
                Affiliations
                GRID grid.5335.0, ISNI 0000000121885934, University of Cambridge, Cavendish Laboratory, ; Cambridge, UK
                Author information
                http://orcid.org/0000-0002-6589-1700
                http://orcid.org/0000-0002-9616-3108
                Article
                19964
                10.1038/s41467-020-19964-7
                7722901
                33293567
                70672d2b-0759-4ab5-9b4a-012062139f07
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 June 2020
                : 4 November 2020
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                © The Author(s) 2020

                Uncategorized
                structure prediction,materials science,computational methods
                Uncategorized
                structure prediction, materials science, computational methods

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