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-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.