High-throughput And Data-driven Machine Learning Techniques For Discovering High-entropy Alloys

This article provides an overview of the materials genome strategy (MGS) applied in high-entropy alloys (HEAs). It discusses the development of HEAs, the application of MGI in this field, and the challenges and opportunities in this interdisciplinary area. The article also explores high-throughput preparation and characterization techniques for HEAs, high-throughput computing methods, and data-driven machine learning strategies. It concludes with an outlook on potential research activities and scientific challenges in the future.