Scientific AI in materials science: a path to a sustainable and scalable paradigm

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that th...

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Bibliographic Details
Published in:Machine learning: science and technology 2020-09, Vol.1 (3), p.33001
Main Authors: DeCost, BL, Hattrick-Simpers, JR, Trautt, Z, Kusne, AG, Campo, E, Green, ML
Format: Article
Language:English
Subjects:
Artificial intelligence
Condensed matter physics
cross-discplinary research
Intersections
Machine learning
Materials science
reproducible science
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Summary:Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g. development of robust, physically meaningful multiscale material representations) to social (e.g. promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ab9a20