Recent advances in modelling structure-property correlations in high-entropy alloys

• Published in: Journal of materials science & technology 2025-01, Vol.204, p.127-151
• Main Authors: Deshmukh, Akash A., Ranganathan, Raghavan
• Format: Article
• Language: English
• Subjects: CALPHAD; DFT; High-entropy alloys; Machine learning; Molecular dynamics; Structure-property correlations
• ISSN: 1005-0302
• DOI: 10.1016/j.jmst.2024.03.027

•This review article provides a comprehensive overview of the recent developments of computational studies of high-entropy alloys.•Application of CALPHAD, molecular dynamics simulations, ab initio calculations across several properties such as phase stability, ductility, thermal, magnetic behavior are also presented.•The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining the a comprehensive set of structure-properties relations.•Finally, the capabilities, limitations and outlook for future studies are also presented. Since antiquity, humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands. In 2004, this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys (HEAs) comprising multi-principal elements. Owing to the four “core-effects”, these alloys exhibit exceptional properties including better structural stability, high strength and ductility, improved fatigue/fracture toughness, high corrosion and oxidation resistance, superconductivity, magnetic properties, and good thermal properties. Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions. However, HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy. Several attempts have been made to understand these alloys by empirical and computational models, and data-driven approaches to accelerate the materials discovery with a desired set of properties. The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations. Additionally, the role of machine learning approaches is also reviewed, underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs, and the scope for future efforts in this direction. [Display omitted]