Pressure-driven transition of shock-induced plasticity in aluminum revealed by a machine learning potential

2026-05-02

PRE

Lilong Luo

Qing-An Li

Xiao-Tong Li *

Shan Zhang

Pengfei Guan *

DOI：10.1016/j.commatsci.2026.114745

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Abstract

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• High-density training data enables a high-fidelity ML potential for Al. • NEMD simulations uncover a pressure-dependent transition in shock-induced plasticity. • Mechanistic transition is attributed to pressure-driven shifts in GSFE. • A physics-informed, data-driven framework provides atomic insight under extreme loading.

Keywords:

machine learning potential

shock-induced plasticity

pressure dependence

aluminum

generalized stacking fault energy

Pressure-driven transition of shock-induced plasticity in aluminum revealed by a machine learning potential

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