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<p>High-entropy materials (HEMs), encompassing alloys, ceramics, and compounds with extreme compositional complexity, have opened unprecedented opportunities for property tailoring through entropy engineering. However, the vast compositional space and strong local chemical and structural heterogeneities render traditional experimental trial-and-error approaches inefficient. Consequently, computational methods have become indispensable for understanding and predicting structure–property correlations in HEMs. This review presents a comprehensive and pedagogical overview of computational approaches employed for HEM design, organised within a hierarchical framework spanning empirical, phenomenological, and mechanistic models. Empirical descriptors and database-driven machine learning techniques are discussed as rapid screening tools, while phenomenological approaches, including CALPHAD, semi-empirical thermodynamic models, strengthening theories, and generative artificial intelligence, are reviewed for their balance between physical insight and computational efficiency. Mechanistic models based on first-principles calculations, atomistic simulations, machine-learned interatomic potentials, and continuum approaches are critically examined for their ability to capture local chemical environments, defects, and microstructural evolution. Emphasis is placed on the role of chemical short-range order, disorder, and multiscale coupling in governing emergent properties. Finally, key challenges and future directions for inverse design, data-efficient modelling, and integrated multiscale frameworks are outlined, highlighting pathways to accelerate the rational discovery and optimisation of high-entropy materials.</p>

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A Comprehensive Review of the Computational Approaches for Structure–Property Prediction of High Entropy Materials

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Suman Chabri,
  • Gautam Anand

摘要

High-entropy materials (HEMs), encompassing alloys, ceramics, and compounds with extreme compositional complexity, have opened unprecedented opportunities for property tailoring through entropy engineering. However, the vast compositional space and strong local chemical and structural heterogeneities render traditional experimental trial-and-error approaches inefficient. Consequently, computational methods have become indispensable for understanding and predicting structure–property correlations in HEMs. This review presents a comprehensive and pedagogical overview of computational approaches employed for HEM design, organised within a hierarchical framework spanning empirical, phenomenological, and mechanistic models. Empirical descriptors and database-driven machine learning techniques are discussed as rapid screening tools, while phenomenological approaches, including CALPHAD, semi-empirical thermodynamic models, strengthening theories, and generative artificial intelligence, are reviewed for their balance between physical insight and computational efficiency. Mechanistic models based on first-principles calculations, atomistic simulations, machine-learned interatomic potentials, and continuum approaches are critically examined for their ability to capture local chemical environments, defects, and microstructural evolution. Emphasis is placed on the role of chemical short-range order, disorder, and multiscale coupling in governing emergent properties. Finally, key challenges and future directions for inverse design, data-efficient modelling, and integrated multiscale frameworks are outlined, highlighting pathways to accelerate the rational discovery and optimisation of high-entropy materials.