Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis
Abstract
Thermolysis and shock loading can produce similar major stable products in energetic materials, despite different chemical evolution and reaction pathways. Here, we develop EMFF-MACE, a reactive machine-learning potential for C/H/N/O energetic materials, enabling thermolysis simulations of 20 materials and shock simulations of 10 on a common potential energy surface. Comparisons at matched normalized reaction progress reveal divergent chemical states and local structural environments. Mean reaction-state similarity decreases from 94.0% at 20% progress to 25.9% at trajectory endpoints, while 47.1-92.6% of shock local environments lie outside corresponding material-specific thermolysis reference domains. Structural and reaction-network analyses reveal enhanced intermolecular reorganization and additional association and rearrangement branches under shock loading, despite partially shared early decomposition chemistry. These findings show that loading history influences the structural states and subsequent reaction pathways accessed during decomposition, rather than merely the rate of thermolysis.
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BibTeX
@misc{wen2026shock,
title = {{Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis}},
author = {Mingjie Wen and Jiahe Han and Zihan Zhou and Hao Wang and Xueyong Guo and Qingzhao Chu and Dongping Chen},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41955},
eprint = {2610.41955},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41955},
}
RIS
TY - UNPB
TI - Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis
AU - Mingjie Wen
AU - Jiahe Han
AU - Zihan Zhou
AU - Hao Wang
AU - Xueyong Guo
AU - Qingzhao Chu
AU - Dongping Chen
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41955
UR - https://arxiv.org/abs/2610.41955
AB - Thermolysis and shock loading can produce similar major stable products in energetic materials, despite different chemical evolution and reaction pathways. Here, we develop EMFF-MACE, a reactive machine-learning potential for C/H/N/O energetic materials, enabling thermolysis simulations of 20 materials and shock simulations of 10 on a common potential energy surface. Comparisons at matched normalized reaction progress reveal divergent chemical states and local structural environments. Mean reaction-state similarity decreases from 94.0% at 20% progress to 25.9% at trajectory endpoints, while 47.1-92.6% of shock local environments lie outside corresponding material-specific thermolysis reference domains. Structural and reaction-network analyses reveal enhanced intermolecular reorganization and additional association and rearrangement branches under shock loading, despite partially shared early decomposition chemistry. These findings show that loading history influences the structural states and subsequent reaction pathways accessed during decomposition, rather than merely the rate of thermolysis.
ER -