Crystal graph attention networks for the prediction of stable materials-Reference-Cited by-同舟云学术

Crystal graph attention networks for the prediction of stable materials

Published:2021-12-03 Issue:49 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Schmidt Jonathan¹^ORCID,Pettersson Love²,Verdozzi Claudio²,Botti Silvana³^ORCID,Marques Miguel A. L.¹^ORCID

Affiliation:

1. Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, 06120 Halle (Saale), Germany.

2. Department of Physics, Lund University Box 118, 221 00 Lund, Sweden.

3. Institut für Festkörpertheorie und Optik and European Theoretical Spectroscopy Facility, Friedrich-Schiller-Universität Jena, D-07743 Jena, Germany.

Abstract

Crystal graph attention networks speed up the prediction of new thermodynamically stable materials in high-throughput searches.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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5. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

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