High-throughput and data-driven machine learning techniques for discovering high-entropy alloys

communications materials Review article https://doi.org/10.1038/s43246-024-00487-3 High-throughput and data-driven machine learning techniques for discovering highentropy alloys Check for updates 1,2 2 1,3 1234567890():,; 1234567890():,; Lu Zhichao , Ma Dong , Liu Xiongjun & Zhaoping Lu 1,3 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 fastgrowing 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 (HEAs), also called multi-principal element alloys1–3, are chemically disordered but topologically ordered with the formation of random solid-solution (SS) structures, such as face-centered cubic (FCC), body-centered cubic (BCC), or hexagonal-close-packed (HCP). Understanding the composition–structure–properties relationship has long been a topic of great interest in HEAs. Thus, 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 the past few years, besides metallic systems, high entropy materials have expanded to ceramics made of carbides, borides, or nitrides of IV and V group transition metals, which have remarkable properties4–6. Due to these unique properties and large composition space, high entropy materials have promising potential applications under extreme conditions, such as, in high-temperature structural components, corrosion-resistant parts, coatings, and nuclear materials7. 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” manner8? (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 9. 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 materials information for the following ML, and vice versa, ML can provide intelligent feedback to the experiments10–12. Through continuing efforts to integrate experiment, computation, and data-driven ML, the underlying structure–property relationships to the materials genome can be revealed and thus seed a new generation of advanced HEAs13. This review aims to present a brief state-of-the-art overview of the materials genome strategy (MGS) applied in HEAs and provide a timely focus on key developments, including challenges and opportunities, in this interdisciplinary area. Specifically, we will give a brief introduction to the development of HEAs and the application of MGI in this field. Additionally, some challenges will also be listed in a brief manner in “Introduction”. In section “High-throughput preparation and characterization of HEAs”, the main high-throughput preparation and characterization techniques for 1 Beijing Advanced Innovation Center for Materials Genome Engineering, State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing, China. 2Songshan Lake Materials Laboratory, Dongguan, China. 3Institute for Materials Intelligent Technology, Liaoning Academy of e-mail: ; Materials, Shenyang, China. Communications Materials | (2024)5:76 1 https://doi.org/10.1038/s43246-024-00487-3 HEAs will be discussed in detailed and critical issues needed to be solved will also be proposed. In section “High-throughput computing for HEAs”, we will present and discuss applications of high-throughput computation method in accelerating the development of HEAs. An in-depth discussion about data-driven ML strategy for HEAs will be provided in section “Datadriven machine learning strategies “. Finally, in “Outlook” section, we will give an outlook of potential research activities to be exploited and main scientific challenges to be addressed in the future. The core purpose underlying the brief review is to provide an important opportunity to advance the understanding of MGS employed in HEAs and to offer researchers a platform to foster new ideas. High-throughput preparation and characterization of HEAs The design of HEAs poses a significant challenge when exploring the phase structure and desirable properties through the vast potential multicomponent compositional space available14. As such, unconventional highthroughput preparation techniques are crucially important, particularly for effectively narrowing down the alloys in a wide composition space. Among these, HEAs exploit a variety of preparation techniques, such as, combinatorial thin film deposition, laser additive manufacturing (LAM), rapid alloying prototype, diffusion multiples, and those based on welding. In what follows, we will give an overview of the different high-throughput techniques that were used to prepare multi-component HEAs and point out some critical issues that needed to be res (...truncated)