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Machine Learning for the Discovery, Design, and Engineering of Materials.
- Source :
-
Annual review of chemical and biomolecular engineering [Annu Rev Chem Biomol Eng] 2022 Jun 10; Vol. 13, pp. 405-429. Date of Electronic Publication: 2022 Mar 23. - Publication Year :
- 2022
-
Abstract
- 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.
- Subjects :
- High-Throughput Screening Assays
Algorithms
Machine Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1947-5446
- Volume :
- 13
- Database :
- MEDLINE
- Journal :
- Annual review of chemical and biomolecular engineering
- Publication Type :
- Academic Journal
- Accession number :
- 35320698
- Full Text :
- https://doi.org/10.1146/annurev-chembioeng-092320-120230