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Machine Learning for the Discovery, Design, and Engineering of Materials

Journal Article · · Annual Review of Chemical and Biomolecular Engineering
 [1];  [1];  [2]
  1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA,, ,; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA,, ,
+ Show Author Affiliations

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Research Organization:
Univ. of Minnesota, Minneapolis, MN (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0012702; SC0018096
OSTI ID:
1980843
Journal Information:
Annual Review of Chemical and Biomolecular Engineering, Vol. 13, Issue 1; ISSN 1947-5438
Publisher:
Annual Reviews
Country of Publication:
United States
Language:
English

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