A Deep Learning Potential for Accurate Shock Response Simulations in Tin

TL;DR

This paper develops a machine learning interatomic potential for tin that accurately simulates shock responses, phase transitions, and thermodynamic properties, bridging the gap between ab initio calculations and large-scale dynamic simulations.

Contribution

It introduces a novel machine learning potential trained with a concurrent learning framework, optimized for shock-response simulations of tin across various phases and conditions.

Findings

Accurately reproduces DFT properties and experimental data.

Effectively models solid-solid phase transitions and shock Hugoniot.

Enables large-scale dynamic simulations with ab initio accuracy.

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.