Explainable machine learning in materials science

Zhong, Xiaoting; Gallagher, Brian; Liu, Shusen; Kailkhura, Bhavya; Hiszpanski, Anna; Han, T. Yong-Jin

doi:10.1038/s41524-022-00884-7

Explainable machine learning in materials science

Journal Article · Thu Sep 22 00:00:00 EDT 2022 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-022-00884-7· OSTI ID:1888837

; Gallagher, Brian; ; Kailkhura, Bhavya; ;

Abstract

Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1888837

Alternate ID(s):: OSTI ID: 2007609

Report Number(s):: LLNL--JRNL-833293; 204; PII: 884

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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