| SPECIAL TOPIC — AI + Physical Science |
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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 |
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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.
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Received: 31 July 2025
Revised: 22 October 2025
Accepted manuscript online: 24 October 2025
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PACS:
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87.10.Tf
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(Molecular dynamics simulation)
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34.20.Cf
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(Interatomic potentials and forces)
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62.50.Ef
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(Shock wave effects in solids and liquids)
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84.35.+i
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(Neural networks)
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| Fund: 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). |
Corresponding Authors:
Han Wang
E-mail: wang_han@iapcm.ac.cn
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Cite this article:
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 2026 Chin. Phys. B 35 018701
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