Structure–property Predictions in Metallic Glasses: Insights from Data-Driven Atomistic Simulations

Gokul Raman Arumugam Kumar, Kanika Arora, Manish Aggarwal, S. Swayamjyoti, Param Punj Singh,Kisor Kumar Sahu,Raghavan Ranganathan

Journal of Materials Research（2024）

Indian Institute of Technology Gandhinagar | Mcgill University | Aspen Technology | RWTH Aachen University | Indian Institute of Technology Bhubaneswar

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Abstract

The field of metallic glasses has been an active area of research owing to the complex structure–property correlations and intricacies surrounding glass formation and relaxation. This review provides a thorough examination of significant works that elucidate the structure–property correlations of metallic glasses, derived from detailed atomistic simulations coupled with data-driven approaches. The review starts with the theoretical and fundamental framework for understanding important properties of metallic glasses such as transition temperatures, relaxation phenomena, the potential energy landscape, structural features such as soft spots and shear transformation zones, atomic stiffness and structural correlations. The need to understand these concepts for leveraging metallic glasses for a wide range of applications such as performance under tensile loading, viscoelastic properties, relaxation behavior and shock loading is also elucidated. Finally, the use of machine learning algorithms in predicting the properties of metallic glasses along with their applications, limitations and scope for future work is presented.

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Key words

Metallic glass,Structure–property relations,Atomistic simulations,Machine learning

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【要点】：本文综述了通过数据驱动原子模拟方法对金属玻璃的结构-性能关系的理解及其应用，揭示了金属玻璃的重要性质和机器学习算法在性能预测中的潜力。

【方法】：采用详细原子模拟结合数据驱动方法，研究金属玻璃的结构-性能相关性。

【实验】：无具体实验描述，综述了使用原子模拟和机器学习算法在预测金属玻璃性能方面的成果。未提及具体数据集名称。