From High-Entropy Alloys to Alloys with High Entropy: A New Paradigm in Materials Science and Engineering for Advancing Sustainable Metallurgy

作     者：Torralba, Jose Manuel Meza, Alberto Sivagnana, Venkatesh Mostafaei, Amir Mohammadzadeh, Ahad 

作者机构：Department of Materials Science and Engineering and Chemical Engineering Universidad Carlos III de Madrid Av. Universidad 30 Leganés28911 Spain Imdea Materials Institute Calle Eric Kandel 2 Getafe Madrid28906 Spain Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology 10 W 32nd Street ChicagoIL60616 United States Department of Materials Engineering Faculty of Engineering University of Maragheh P.O. Box 83111 Maragheh55181 Iran 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

核心收录：

主　　题：Intermetallics 

摘      要：The development of high-entropy alloys (HEAs) has marked a paradigm shift in alloy design, moving away from traditional methods that prioritize a dominant base metal enhanced by minor elements. HEAs instead incorporate multiple alloying elements with no single dominant component, broadening the scope of alloy design. This shift has led to the creation of diverse alloys with high entropy (AHEs) families, including high-entropy steels, superalloys, and intermetallics, each highlighting the need to consider additional factors such as stacking fault energy (SFE), lattice misfit, and anti-phase boundary energy (APBE) due to their significant influence on microstructure and performance. Leveraging multiple elements in alloying opens up promising possibilities for developing new alloys from multi-component scrap and electronic waste, reducing reliance on critical metals and emphasizing the need for advanced data generation techniques. With the vast possibilities offered by these multi-component feedstocks, modelling and Artificial Intelligence based tools are essential to efficiently explore and optimize new alloys, supporting sustainable progress in metallurgy. These advancements call for a reimagined alloy design framework, emphasizing robust data acquisition, alternative design parameters, and advanced computational tools over traditional composition-focused methodologies. © 2025, CC BY.