An ab-initio deep neural network potential to study the effect of density on the thermal decomposition mechanism of RDX

ZY Chen and HX Liu and YH Ma and FJ Shang and MH Lv and DN Song and SH Yin and JY Liu, CHEMICAL PHYSICS LETTERS, 876, 142222 (2025).

DOI: 10.1016/j.cplett.2025.142222

A novel neural network potential (NNP) model is developed to analyze the thermal decomposition mechanism in multi-density Cyclotrimethylenetrinitramine (RDX). The model combines the accuracy of ab initio molecular dynamics (AIMD) with the efficiency of empirical force fields. Simulations indicate that increased density accelerates reaction kinetics but reduces reaction completion. A reaction network with two primary channels was constructed by evaluating critical energy barriers using transition state theory and quantum chemical analysis. Results demonstrate that the NNP method accurately captures microscopic mechanisms, such as C-NO2 bond cleavage and NO2 radical chain reactions, providing a universal framework for studying the C/H/O/N-based materials.

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