Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential

doi:10.1088/1674-1056/ae1726

中国物理B ›› 2026, Vol. 35 ›› Issue (1): 18701-018701.doi: 10.1088/1674-1056/ae1726

Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential

Enze Hou(侯恩则)^1,2, Xiaoyang Wang(王啸洋)³, and Han Wang(王涵)^3,4,†

1 Institute of Applied Physics and Computational Mathematics, Beijing 100094, China;
2 Graduate School of China Academy of Engineering Physics, Beijing 100088, China;
3 Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100094, China;
4 HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing 100871, China

收稿日期:2025-07-31 修回日期:2025-10-22 接受日期:2025-10-24 发布日期:2026-01-05
通讯作者: Han Wang E-mail:wang_han@iapcm.ac.cn
基金资助:
This project was supported by the National Key R&D Program of China (Grant No. 2022YFA1004300) and the National Natural Science Foundation of China (Grant No. 12404004).

Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential

Enze Hou(侯恩则)^1,2, Xiaoyang Wang(王啸洋)³, and Han Wang(王涵)^3,4,†

1 Institute of Applied Physics and Computational Mathematics, Beijing 100094, China;
2 Graduate School of China Academy of Engineering Physics, Beijing 100088, China;
3 Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100094, China;
4 HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing 100871, China

Received:2025-07-31 Revised:2025-10-22 Accepted:2025-10-24 Published:2026-01-05
Contact: Han Wang E-mail:wang_han@iapcm.ac.cn
Supported by:
This project was supported by the National Key R&D Program of China (Grant No. 2022YFA1004300) and the National Natural Science Foundation of China (Grant No. 12404004).

摘要/Abstract

摘要： Lead (Pb) is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses. Under shock-wave loading, its dynamic mechanical behavior comprises two key phenomena: plastic deformation and shock-induced phase transitions. The underlying mechanisms of these processes are still poorly understood. Revealing these mechanisms remains challenging for experimental approaches. Non-equilibrium molecular dynamics (NEMD) simulations are an alternative theoretical tool for studying dynamic responses, as they capture atomic-scale mechanisms such as defect evolution and deformation pathways. However, due to the limited accuracy of empirical interatomic potentials, the reliability of previous NEMD studies has been questioned. Using our newly developed machine learning potential for Pb-Sn alloys, we revisited the microstructural evolution in response to shock loading under various shock orientations. The results reveal that shock loading along the [001] orientation of Pb exhibits a fast, reversible, and massive phase transition and stacking-fault evolution. The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation. Loading along the [011] orientation leads to slow, irreversible plastic deformation, and a localized FCC-BCC phase transition in the Pitsch orientation relationship. This study provides crucial theoretical insights into the dynamic mechanical response of Pb, offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions.

关键词: interatomic potentials, molecular dynamics, shock impacts, machine learning

Abstract: Lead (Pb) is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses. Under shock-wave loading, its dynamic mechanical behavior comprises two key phenomena: plastic deformation and shock-induced phase transitions. The underlying mechanisms of these processes are still poorly understood. Revealing these mechanisms remains challenging for experimental approaches. Non-equilibrium molecular dynamics (NEMD) simulations are an alternative theoretical tool for studying dynamic responses, as they capture atomic-scale mechanisms such as defect evolution and deformation pathways. However, due to the limited accuracy of empirical interatomic potentials, the reliability of previous NEMD studies has been questioned. Using our newly developed machine learning potential for Pb-Sn alloys, we revisited the microstructural evolution in response to shock loading under various shock orientations. The results reveal that shock loading along the [001] orientation of Pb exhibits a fast, reversible, and massive phase transition and stacking-fault evolution. The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation. Loading along the [011] orientation leads to slow, irreversible plastic deformation, and a localized FCC-BCC phase transition in the Pitsch orientation relationship. This study provides crucial theoretical insights into the dynamic mechanical response of Pb, offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions.

Key words: interatomic potentials, molecular dynamics, shock impacts, machine learning

中图分类号: (Molecular dynamics simulation)

87.10.Tf

34.20.Cf (Interatomic potentials and forces) 62.50.Ef (Shock wave effects in solids and liquids) 84.35.+i (Neural networks)

Enze Hou(侯恩则), Xiaoyang Wang(王啸洋), and Han Wang(王涵). Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential[J]. 中国物理B, 2026, 35(1): 18701-018701.

Enze Hou(侯恩则), Xiaoyang Wang(王啸洋), and Han Wang(王涵). Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential[J]. Chin. Phys. B, 2026, 35(1): 18701-018701.

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Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential

Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential

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