Jiahe Han | Searcharxiv

arXiv2026

Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis

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.

cond-mat.mtrl-sci↗

arXiv2025

A General Neural Network Potential for Energetic Materials with C, H, N, and O elements

The discovery and optimization of high-energy materials (HEMs) are constrained by the prohibitive computational expense and prolonged development cycles inherent in conventional approaches. In this work, we develop a general neural network potential (NNP) that efficiently predicts the structural, mechanical, and decomposition properties of HEMs composed of C, H, N, and O. Our framework leverages pre-trained NNP models, fine-tuned using transfer learning on energy and force data derived from density functional theory (DFT) calculations. This strategy enables rapid adaptation across 20 different HEM systems while maintaining DFT-level accuracy, significantly reducing computational costs. A key aspect of this work is the ability of NNP model to capture the chemical activity space of HEMs, accurately describe the key atomic interactions and reaction mechanisms during thermal decomposition. The general NNP model has been applied in molecular dynamics (MD) simulations and validated with experimental data for various HEM structures. Results show that the NNP model accurately predicts the structural, mechanical, and decomposition properties of HEMs by effectively describing their chemical activity space. Compared to traditional force fields, it offers superior DFT-level accuracy and generalization across both microscopic and macroscopic properties, reducing the computational and experimental costs. This work provides an efficient strategy for the design and development of HEMs and proposes a promising framework for integrating DFT, machine learning, and experimental methods in materials research. (To facilitate further research and practical applications, we open-source our NNP model on GitHub: https://github.com/MingjieWen/General-NNP-model-for-C-H-N-O-Energetic-Materials.)

cond-mat.mtrl-sci↗