skip to main content


Title: Machine learning in materials informatics: recent applications and prospects

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, withmore » particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.« less
 [1] ;  [1] ;  [2] ;  [3] ;  [1]
  1. Univ. of Connecticut, Storrs, CT (United States). Dept. of Materials Science & Engineering. Inst. of Materials Science
  2. Fritz Haber Inst. of the Max Planck Society, Berlin (Germany); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Univ. of Connecticut, Storrs, CT (United States). Dept. of Materials Science & Engineering. Inst. of Materials Science; Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 2057-3960; TRN: US1800906
Grant/Contract Number:
AC52-06NA25396; N00014-14-1-0098; N00014-16-1-2580; N00014-10-1-0944
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 3; Journal ID: ISSN 2057-3960
Nature Publishing Group
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE; Office of Naval Research (ONR) (United States)
Country of Publication:
United States
36 MATERIALS SCIENCE; materials science; techniques and instrumentation
OSTI Identifier: