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Growing field of materials informatics: databases and artificial intelligence

Published online by Cambridge University Press:  14 January 2020

Alejandro Lopez-Bezanilla Open the ORCID record for Alejandro Lopez-Bezanilla [Opens in a new window]  and
Peter B. Littlewood Open the ORCID record for Peter B. Littlewood [Opens in a new window]
Alejandro Lopez-Bezanilla*
Affiliation:
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM87545, USA
Peter B. Littlewood
Affiliation:
Argonne National Laboratory, Lemont, IL60439, USA James Franck Institute, University of Chicago, Chicago, IL60637, USA
*
Address all correspondence to Alejandro Lopez-Bezanilla at alejandrolb@gmail.com
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Abstract

The paradigm of molecular discovery in the chemical and pharmaceutical industry has followed a repetitive succession of screening and synthesis, involving the analysis of individual molecules that were both natural and produced. This ability to generate and screen libraries of compounds has found an echo in solid-state physics with the demand to explore and produce new materials for testing. In response to this demand, a golden age of materials discovery is being developed, with progress on important areas of both basic science and device applications. The confluence of theoretical and simulation methods, together with the availability of computation resources, has established the “materials genome” approach that is used by a growing number of research groups around the world with the goal of innovating on materials through systematic discovery. In this Prospective, an overview of this group of methodologies in tackling the ever-increasing complexity of computational materials science simulations is provided. Computational simulation is highlighted as a major component of rational design and synthesis of new materials with targeted properties, describing progress on databases and large data treatment. Tools for new materials discovery, including progress on the deployment of new data repositories, the implementation of high-throughput simulation approaches, and the development of artificial intelligence algorithms, are discussed.

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Artificial Intelligence Prospective Article
Information
MRS Communications , Volume 10 , Issue 1 , March 2020 , pp. 1 - 10
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Copyright © Materials Research Society 2020

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