Deep learning potential for accurate shock response simulations in tin
Phys. Rev. Materials 10, 023605 – Published 25 February, 2026
DOI: https://doi.org/10.1103/6psb-3ksf
Abstract
Tin (Sn) plays a crucial role in studying the dynamic mechanical responses of ductile metals under shock loading. Atomistic simulations serve to unveil the nanoscale 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 from 0 to 5000 K, encompassing both solid and liquid phases as well as structures with free surfaces. It accurately reproduces density functional theory–derived basic properties, experimental melting curves, solid-solid phase boundaries, and shock Hugoniot results. This demonstrates the model's potential to bridge initio precision with large-scale dynamic simulations of Sn.
Physics Subject Headings (PhySH)
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This article appears in the following collection:
Machine Learning for Materials Discovery and Understanding
The Editors of Physical Review Materials are pleased to present the Collection on Machine Learning for Materials Discovery and Understanding, highlighting cutting-edge advances in machine learning method development and applications for materials discovery and fundamental understanding of the structure-property-function relationship. The Collection is being guest-edited by Deyu Lu of Brookhaven National Laboratory (USA) and Jinlan Wang of Southeast University (China). Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review Materials editorial team managed the peer review and made all editorial decisions.
