Machine-Learning Force Fields for Metallic Materials: Phase Transformations and Deformations

ZS Li and L Zhao and HX Zong and XD Ding, ACTA METALLURGICA SINICA, 60, 1388-1404 (2024).

DOI: 10.11900/0412.1961.2024.00139

A comprehensive understanding of the microscopic mechanisms underlying phase transitions and deformations in metallic materials is crucial for developing new materials that meet the nation's essential needs. Molecular dynamic simulation techniques, particularly those powered by machine- learning molecular force fields, are emerging as potent tools for unraveling atomic-scale phenomena. In this study, recent advancements in machine-learning molecular force fields were reviewed to investigate metallic phase transitions and deformations. First, the fundamental principles and evolution of machine- learning molecular force fields were introduced. Then, the phase transformation and deformation of metals were examined, providing insights into the kinetics of phase transitions and microscopic mechanisms. Finally, the challenges faced by current machine-learning molecular force fields in studying metallic phase transformations and deformations were identified, and a glimpse into future research directions was discussed.

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