Molecular modelling of high-entropy alloys: from quantum to atomistic, and machine learning
Abstract
Abstract High-entropy alloys (HEAs), characterized by complex multielement compositions, offer broad opportunities to tune mechanical, corrosion, thermal, wetting, and functional properties, while their vast configurational space makes molecular modelling essential. This review evaluates progress across computational thermodynamics, density functional theory, atomistic simulations, and machine learning approaches, highlighting multiscale integration, methodological advances, current challenges, validation needs, and future directions for predictive HEA modelling and materials design across industrial applications and engineering practice.
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Authors: Michele Pellegrino, Mokhtar Ganjali Koli, Fabiano Tarulli, Joseph E. Arnold, Dumitru Mitrică, Luca Bergamasco, Gonzalo García Fuentes, Radu Piticescu, Beñat Gurrutxaga-Lerma, Matteo Fasano
Institutions: University of Birmingham, Politecnico di Torino, Institute of Non-ferrous and Rare Metals, Asociación de la Industria Navarra