Knowledge-Guided Construction of Machine Learning Potentials for Reactive Behavior in Energetic Materials

Guanchen Dong; Huanrong Liu; Lin Wang; Yuxuan Chen; Zhengyu Tu; Qing-an Li; Bin Xu; Jian Li; Shan Zhang; Xiaotong Li; Linghua Tan; Guangcheng Yang; Jun Kang; Rui Su; Pengfei Guan

arXiv · 2610.17987

Knowledge-Guided Construction of Machine Learning Potentials for Reactive Behavior in Energetic Materials

Guanchen Dong·Huanrong Liu·Lin Wang·Yuxuan Chen·Zhengyu Tu·Qing-an Li·Bin Xu·Jian Li·Shan Zhang·Xiaotong Li·Linghua Tan·Guangcheng Yang·Jun Kang·Rui Su·Pengfei Guan

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Abstract

Energetic materials involve multiple interactions, making reliable machine-learning potentials (MLPs) difficult to construct without extensive datasets. Here, we enhance the ability of the neural network interatomic potential (NNAP) to describe molecular systems using sphmulti, a refined spherical-harmonic basis set that combines extra radial basis with element-specific chemical weighting in each radial channel. We further integrate hyperactive learning, farthest-point sampling, and iterative sufficiency criteria with knowledge-guided key events, enabling the NNAP to retain essential information for describing the material system. For 3,3'-diamino-4,4'-azoxyfurazan, the resulting NNAP is trained on only 13,680 configurations yet captures key properties, including the isomerization barrier, pyrolysis activation energy, and shock Hugoniot behavior. NNAP-driven molecular dynamics simulations further reveal that pyrolysis proceeds through furazan-ring opening followed by chain reactions, whereas shock loading induces anisotropic hydrogen transfer driven by interlayer structural deformations. This work enables efficient and reliable MLPs for energetic materials.

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Guanchen Dong, Huanrong Liu, Lin Wang, Yuxuan Chen, Zhengyu Tu, Qing-an Li, Bin Xu, Jian Li, Shan Zhang, Xiaotong Li, Linghua Tan, Guangcheng Yang, Jun Kang, Rui Su, Pengfei Guan. 2026-10-13. Knowledge-Guided Construction of Machine Learning Potentials for Reactive Behavior in Energetic Materials. https://arxiv.org/abs/2610.17987

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