A neural network potential model for CL-20/DNT high-energy material: thermal stability regulation and decomposition mechanism

YF Zhang and HY Liu and MH Lv and HX Liu and YH Ma and FJ Shang and JY Liu and WZ Li, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 27, 21994-22003 (2025).

DOI: 10.1039/d5cp02224j

To elucidate the detailed thermal decomposition mechanism of the CL-20/DNT cocrystal material, a neural network potential (NNP) model was developed. This NNP model has been demonstrated to exhibit excellent computational efficiency while retaining the accuracy of ab initio. Molecular dynamics simulations over a wide temperature range were performed based on the trained NNP model. The results show that the intermolecular hydrogen bond interaction formed between DNT and CL-20 during the initial decomposition process and the DNT capture of the NO2 group released from CL-20 together enhance the stability of the cocrystal system. The detailed formation pathways for the major products, including N2, H2O, CO2, and HNCO, were obtained by product analysis. Furthermore, temperature was found to influence the quantity variation of CO2, and part of CO2 is converted to CO at high temperature. By analyzing the clusters, it is found that the C rings or C-N rings are more easily formed at the temperature of 2200 K, and some O atoms will combine with the clusters at the temperature of 3000 K. This study investigates the complex reaction dynamics of CL-20/DNT from an atomic perspective, and the methodology can be extended to a broader range of energetic material systems.

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