High-throughput and data-driven machine learning techniques for discovering high-entropy alloys High-entropy alloys (HEAs) have attracted significant attention due to their unique chemical, physical, and mechanical properties. Understanding the structure-property relationship in HEAs is crucial for discovering new compositions with desirable properties. The materials genome strategy has been increasingly used to discover new HEAs with better performance. This review provides an overview of key advances in this fast-growing area, along with current challenges and potential opportunities for HEAs. It discusses topics such as high-throughput preparation, characterization, and computation of HEAs, and data-driven machine learning for accelerating alloy development. Future research directions and perspectives for the materials genome-assisted design of HEAs are proposed. HEAs, also called multi-principal element alloys, are chemically disordered but topologically ordered with the formation of random solid-solution structures. Understanding the composition-structure-property relationship has long been a topic of interest in HEAs. Extensive studies have been carried out on various HEAs, and many attractive properties have been achieved in the last two decades. These properties include good plasticity, high strength and hardness, outstanding high-temperature-softening resistance, and unique electrical and magnetic properties. In recent years, high entropy materials have expanded to ceramics made of carbides, borides, or nitrides of IV and V group transition metals, which have remarkable properties. However, with regard to the property-oriented designs of HEAs, some challenges remain to be solved. (1) Owing to the chemically disordered structure, HEAs are not necessarily equimolar compositions; that is, many potential elements in the periodic table can conceivably be incorporated into HEAs via microalloying or principal element substitution. Therefore, an essentially infinite number of HEAs are available. Since the compositions of HEAs can be continuously adjustable, the properties of interest can be optimized. Conceptually, this poses a serious challenge—How can potential HEAs with properties of interest be fine-tuned efficiently in such a large composition space rather than in a conventional “trial and error” manner? (2) Coupled with the fact that fully understanding the complicated interplay between constituents and properties is a prerequisite when designing new HEAs, How can the intrinsic relationship in a vast and complex database be uncovered? To date, inspired by the Materials Genome Initiative (MGI), high-throughput techniques (preparation, characterization, and calculation) and the data-driven machine learning (ML) method have been adopted by synergistically combining experiment, theory, and computation in a tightly integrated and high-throughput manner, and to predict and optimize HEAs at an unparalleled scale and in an effective way. These tools can be used to screen extensive composition space for a desired property and simultaneously pinpoint specific alloys with the desired properties. Specifically, high-throughput techniques are able to bridge the gap between experiments and ML modeling; that is, high-throughput approaches can provide valuableHigh-throughput and data-driven machine learning techniques for discovering high-entropy alloys High-entropy alloys (HEAs) have attracted significant attention due to their unique chemical, physical, and mechanical properties. Understanding the structure-property relationship in HEAs is crucial for discovering new compositions with desirable properties. The materials genome strategy has been increasingly used to discover new HEAs with better performance. This review provides an overview of key advances in this fast-growing area, along with current challenges and potential opportunities for HEAs. It discusses topics such as high-throughput preparation, characterization, and computation of HEAs, and data-driven machine learning for accelerating alloy development. Future research directions and perspectives for the materials genome-assisted design of HEAs are proposed. HEAs, also called multi-principal element alloys, are chemically disordered but topologically ordered with the formation of random solid-solution structures. Understanding the composition-structure-property relationship has long been a topic of interest in HEAs. Extensive studies have been carried out on various HEAs, and many attractive properties have been achieved in the last two decades. These properties include good plasticity, high strength and hardness, outstanding high-temperature-softening resistance, and unique electrical and magnetic properties. In recent years, high entropy materials have expanded to ceramics made of carbides, borides, or nitrides of IV and V group transition metals, which have remarkable properties. However, with regard to the property-oriented designs of HEAs, some challenges remain to be solved. (1) Owing to the chemically disordered structure, HEAs are not necessarily equimolar compositions; that is, many potential elements in the periodic table can conceivably be incorporated into HEAs via microalloying or principal element substitution. Therefore, an essentially infinite number of HEAs are available. Since the compositions of HEAs can be continuously adjustable, the properties of interest can be optimized. Conceptually, this poses a serious challenge—How can potential HEAs with properties of interest be fine-tuned efficiently in such a large composition space rather than in a conventional “trial and error” manner? (2) Coupled with the fact that fully understanding the complicated interplay between constituents and properties is a prerequisite when designing new HEAs, How can the intrinsic relationship in a vast and complex database be uncovered? To date, inspired by the Materials Genome Initiative (MGI), high-throughput techniques (preparation, characterization, and calculation) and the data-driven machine learning (ML) method have been adopted by synergistically combining experiment, theory, and computation in a tightly integrated and high-throughput manner, and to predict and optimize HEAs at an unparalleled scale and in an effective way. These tools can be used to screen extensive composition space for a desired property and simultaneously pinpoint specific alloys with the desired properties. Specifically, high-throughput techniques are able to bridge the gap between experiments and ML modeling; that is, high-throughput approaches can provide valuable