Material machine learning for alloys: Applications, challenges and perspectives

Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to efficiently design novel materials with superior performance. Here we reviewed the recent applications of ML-assisted design of high-entropy alloys, titanium alloys, copper alloys, aluminum alloys and magnesium alloys. A representative workflow of ML approaches was illustrated to explain the key steps in alloys investigations. Then the current applications of ML in studying five types of alloys were summarized with a broad overview of the best practices via diverse ML techniques. It could be concluded that materials ML for alloys would be full of challenges and opportunities in the development of diverse alloys with low cost. [Display omitted]