An equivariant graph neural network for the elasticity tensors of all seven crystal systems

Wen, Mingjian; Horton, Matthew K.; Munro, Jason M.; Huck, Patrick; Persson, Kristin A.

doi:10.1039/D3DD00233K

Title: An equivariant graph neural network for the elasticity tensors of all seven crystal systems

Journal Article · 15 May 2024 · Digital Discovery

DOI: https://doi.org/10.1039/D3DD00233K · OSTI ID:2294134

^[1];

^[2]; Munro, Jason M. ^[3];

^[4];

^[5]

Chemical and Biomolecular Engineering, University of Houston, Houston, 77204, TX, USA
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA, Microsoft Research, Redmond, 98052, WA, USA
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA
Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA
Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA, Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, 94720, CA, USA

An equivariant graph neural network model enables the rapid and accurate prediction of complete fourth-rank elasticity tensors of inorganic materials, facilitating the discovery of materials with exceptional mechanical properties.

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Sponsoring Organization:: USDOE

OSTI ID:: 2294134

Journal Information:: Digital Discovery, Journal Name: Digital Discovery Vol. 3 Journal Issue: 5; ISSN 2635-098X

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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