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

Abstract

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.