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Updated: Jun 30, 2026

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
Published on: April 1, 2017
Impact-induced hotspot formation and sensitivity descriptor in energetic crystals revealed by deep potential
Wenjuan Li1, Mingjie Wen1, Jiahe Han1
1State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, P. R. China. dc516@bit.edu.cn.
None:
Understanding the microscopic origin of impact sensitivity (IS) in energetic materials (EMs) requires a physically meaningful descriptor that links molecular-scale dynamic response to macroscopic behavior. In this work, molecular dynamics simulations based on a Deep potential (DeepMD) were employed to investigate the impact response of α-RDX nanocrystals under practical drop-weight-like loading conditions. An explicit atomic impactor was introduced to capture heterogeneous mechanical responses, including stress concentration, energy localization, and compression-shear coupled deformation. The simulations reveal that impact-induced reaction initiation proceeds through a sequence of impact energy deposition, mechanical compression, hotspot formation, and rapid decomposition. These processes collectively reflect the intrinsic resistance of material to impact-induced failure at the molecular scale. The decomposition fraction is used as an observable to identify the onset of irreversible reactions, from which the critical impact velocity (vc) is defined. vc serves as a molecular-level descriptor of impact sensitivity that quantifies the material resistance to impact-induced failure, providing a physically interpretable measure of impact resistance. Comparative simulations on eight energetic crystals (IS = 3.5-120 J) reproduce the experimental impact sensitivity ranking with a good correlation (R2 = 0.91) between the descriptor vc2 and IS. These results establish a direct link between atomistic failure processes and macroscopic impact sensitivity, providing a descriptor-based framework for the quantitative prediction and virtual screening of EMs with improved safety-performance balance.
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