Title: Evolution of artificial intelligence for application in contemporary materials science

Journal Article · · MRS communications
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Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Research Organization:
Northwestern Univ., Evanston, IL (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0019358; SC0021399
OSTI ID:
2203571
Journal Information:
MRS communications, Journal Name: MRS communications Journal Issue: 5 Vol. 13; ISSN 2159-6867
Publisher:
Cambridge University Press (CUP)Copyright Statement
Country of Publication:
United States
Language:
English

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