Materials informatics: From the atomic-level to the continuum

Rickman, J. M.; Lookman, T.; Kalinin, S. V.

doi:10.1016/j.actamat.2019.01.051

Title: Materials informatics: From the atomic-level to the continuum

Journal Article · Wed Jan 30 00:00:00 EST 2019 · Acta Materialia

DOI:https://doi.org/10.1016/j.actamat.2019.01.051· OSTI ID:1875378

Rickman, J. M. ^[1]; Lookman, T. ^[2];

^[3]

Lehigh University, Bethlehem, PA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1875378

Alternate ID(s):: OSTI ID: 1547813

Journal Information:: Acta Materialia, Vol. 168, Issue 1; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 83 works

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