Recent progress in the machine learning-assisted rational design of alloys

Alloys designed with the traditional trial and error method have encountered several problems, such as long trial cycles and high costs. The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials, that is, machine learning-as...

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Bibliographic Details
Published in:International journal of minerals, metallurgy and materials metallurgy and materials, 2022-04, Vol.29 (4), p.635-644
Main Authors: Fu, Huadong, Zhang, Hongtao, Wang, Changsheng, Yong, Wei, Xie, Jianxin
Format: Article
Language:English
Subjects:
Alloy development
Alloys
Artificial intelligence
Ceramics
Characterization and Evaluation of Materials
Chemistry and Materials Science
Composites
Composition
Corrosion and Coatings
Design
Design optimization
Glass
Invited Review
Iterative methods
Machine learning
Materials Science
Metallic Materials
Modelling
Multiple objective analysis
Natural Materials
Process parameters
Surfaces and Interfaces
Thin Films
Trial and error methods
Tribology
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Summary:Alloys designed with the traditional trial and error method have encountered several problems, such as long trial cycles and high costs. The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials, that is, machine learning-assisted design. In this paper, the basic strategy for the machine learning-assisted rational design of alloys was introduced. Research progress in the property-oriented reversal design of alloy composition, the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors, and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed. Results showed the great advantages of machine learning, including high efficiency and low cost. Future development trends for the machine learning-assisted rational design of alloys were also discussed. Interpretable modeling, integrated modeling, high-throughput combination, multi-objective optimization, and innovative platform building were suggested as fields of great interest.
ISSN:1674-4799
1869-103X
DOI:10.1007/s12613-022-2458-8