A Deep Learning Potential for Accurate Shock Response Simulations in Tin

2025/05/19 by Yixin Chen, Chen, Yixin, Xiaoyang Wang +6
Earth and Planetary Sciences · Materials Science · #Boron and Carbon Nanomaterials Research #FOS: Physical sciences #High-pressure geophysics and materials #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2505.12698

openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Tin (Sn) plays a crucial role in studying the dynamic mechanical responses of ductile metals under shock loading. Atomistic simulations serves to unveil the nano-scale mechanisms for critical behaviors of dynamic responses. However, existing empirical potentials for Sn often lack sufficient accuracy when applied in such simulation. Particularly, the solid-solid phase transition behavior of Sn poses significant challenges to the accuracy of interatomic potentials. To address these challenges, this study introduces a machine-learning potential model for Sn, specifically optimized for shock-response simulations. The model is trained using a dataset constructed through a concurrent learning framework and is designed for molecular simulations across thermodynamic conditions ranging from 0 to 100 GPa and 0 to 5000 K, encompassing both solid and liquid phases as well as structures with free surfaces. It accurately reproduces density functional theory (DFT)-derived basic properties, experimental melting curves, solid-solid phase boundaries, and shock Hugoniot results. This demonstrates the model's potential to bridge ab initio precision with large-scale dynamic simulations of Sn.

Citations

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• Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
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