Advancing High‐Entropy Alloy Design: From Experimental Methods to Machine‐Learning Integration

This cover illustrates the evolving landscape of high-entropy alloy (HEA) design, a field defined by multi-principal-element chemistries, complex microstructures, and exceptional properties. At the base, the crucible represents the experimental roots of metallurgy: melting, alloying, and iterative characterization that guided early HEA discovery. Rising from it is a cloud in which atoms merge into a multi-element cluster, symbolizing the core of HEAs: intricate interactions, high configurational entropy, and unique property combinations. As the formation ascends, it transitions into a digital neural lattice, capturing the shift toward computational modeling, including machine learning driven approaches in composition design, microstructure analysis, property, and process optimization. This transition emphasizes how artificial intelligence accelerates HEA design exploration and integrates experimental and computational insights into predictive and interpretable design frameworks. The gradient from warm bronze to cool blue background reinforces this progression, from the traditional trial-and-error technique to deep learning architectures, hybrid physics-ML models, explainable AI, generative algorithms, and active-learning strategies. HEAs find potential applications in high-strength structural parts, high-temperature components, corrosion & wear resistant coatings, energy & catalytic systems, electronics, and biomedical devices. (https://doi.org/10.1002/rar2.70048)