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RESEARCH PAPER · Metals and Materials International · 2025

Machine Learning-Driven Design and Discovery of High-Entropy Alloys: A Critical Review of Recent Advances and Future Perspectives

Mohammad Javad Akbari

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Machine Learning-Driven Design and Discovery of High-Entropy Alloys: A Critical Review of Recent Advances and Future PerspectivesOriginalA Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data0Accelerating inverse design of wear resistant high entropy alloys via multiscale informatics2Laser Additively Manufactured High-Entropy Alloys via Laser Powder Bed Fusion and Laser-Directed Energy Deposition: Process–Structure–Property Relationships and Design Strategies1Machine learning-driven accelerated discovery of high-entropy alloys: From composition engineering to multifunctional property prediction0Atomistic simulations of high-entropy alloys: from density functional theory to machine-learning interatomic potentials0

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  1. A Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data

    Journal of Computational and Data-Driven Materials Engineering · 2025-01-01

    Elena Petrova, Ivan Georgiev, Nikolay Stoyanov, Petar Kolev

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  2. Accelerating inverse design of wear resistant high entropy alloys via multiscale informatics

    Journal of Alloys and Compounds · 2026-05-19

    Haobing Hu, Xiaoliang Shi, Chaohua Wu, Yuxuan Chen, Haowen Qin, Wenzheng Zhai, Xiyao Liu, Ahmed Mohamed Mahmoud Ibrahim

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  3. Laser Additively Manufactured High-Entropy Alloys via Laser Powder Bed Fusion and Laser-Directed Energy Deposition: Process–Structure–Property Relationships and Design Strategies

    Materials · 2026-07-26

    Meng-Yun Lee, Hyoung Seop Kim, An‐Chou Yeh

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  4. Machine learning-driven accelerated discovery of high-entropy alloys: From composition engineering to multifunctional property prediction

    Journal of Alloys and Compounds · 2026-08-12

    Reliance Jain, Rakesh Arya, Ahmed Farid Ibrahim, M.A. Hussein

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  5. Atomistic simulations of high-entropy alloys: from density functional theory to machine-learning interatomic potentials

    Journal of Materials Science · 2026-08-29

    Yuji Ikeda, Xiang Xu, Pranav Kumar, Jörg Neugebauer, Li Zhu, Xi Zhang, Fritz Körmann, Blazej Grabowski

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