Learning atoms for materials discovery

Zhou, Quan; Tang, Peizhe; Liu, Shenxiu; Pan, Jinbo; Yan, Qimin; Zhang, Shou-Cheng

doi:10.1073/pnas.1801181115

Title: Learning atoms for materials discovery

Journal Article · 25 June 2018 · Proceedings of the National Academy of Sciences of the United States of America

DOI: https://doi.org/10.1073/pnas.1801181115 · OSTI ID:1457210

Zhou, Quan ^[1]; Tang, Peizhe ^[1]; Liu, Shenxiu ^[1]; Pan, Jinbo ^[2]; Yan, Qimin ^[2]; Zhang, Shou-Cheng ^[3]

Department of Physics, Stanford University, Stanford, CA 94305-4045,
Department of Physics, Temple University, Philadelphia, PA 19122,
Department of Physics, Stanford University, Stanford, CA 94305-4045,, Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025

Significance Motivated by the recent achievements of artificial intelligence (AI) in linguistics, we design AI to learn properties of atoms from materials data on its own. Our work realizes knowledge representation of atoms via computers and could serve as a foundational step toward materials discovery and design fully based on machine learning.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-76SF00515; SC0012575

OSTI ID:: 1457210

Alternate ID(s):: OSTI ID: 1463356

Journal Information:: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 28 Vol. 115; ISSN 0027-8424

Publisher:: Proceedings of the National Academy of SciencesCopyright Statement

Country of Publication:: United States

Language:: English

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