Machine learning in materials science: From explainable predictions to autonomous design

Pilania, Ghanshyam

doi:10.1016/j.commatsci.2021.110360

Machine learning in materials science: From explainable predictions to autonomous design

Journal Article · Wed Mar 10 04:00:00 EST 2021 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2021.110360· OSTI ID:1995143

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

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

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1995143

Alternate ID(s):: OSTI ID: 1815228

Report Number(s):: LA-UR-20-28959

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Vol. 193; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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