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High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
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High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
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Journal Article
High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
2024
Overview
High-entropy alloys (HEAs) have attracted extensive attention in recent decades due to their unique chemical, physical, and mechanical properties. An in-depth understanding of the structure–property relationship in HEAs is the key to the discovery and design of new compositions with desirable properties. Related to this, materials genome strategy has been increasingly used for discovering new HEAs with better performance. This review paper provides an overview of key advances in this fast-growing area, along with current challenges and potential opportunities for HEAs. We also discuss related topics, such as high-throughput preparation, characterization, and computation of HEAs, and data-driven machine learning for accelerating alloy development. Finally, future research directions and perspectives for the materials genome-assisted design of HEAs are proposed and discussed.High-entropy alloys exhibit attractive property combinations. This review paper discusses the use of the materials genome strategy for identifying promising high-entropy alloys, including high-throughout synthesis, characterization, and data-driven machine learning.
Publisher
Nature Publishing Group
