DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials

MY Guo and YF Yan and P Chen and WX Zhang, NPJ COMPUTATIONAL MATERIALS, 11, 246 (2025).

DOI: 10.1038/s41524-025-01739-7

Atomic-scale insight into decompositions in energetic materials (EMs) is essential for harnessing energy release, which remains elusive due to both instrumental and computational limitations. Herein, we developed DeepEMs-25, a deep-learning potential trained on diverse EMs towards accurate and efficient simulations. Applying DeepEMs-25 to an isostructural ABX3 molecular perovskites series, with A-site organic cations, B-site alkali or ammonium cations, and X-site perchlorate anions, we probe the effect of cation size on reactivity. Arrhenius analysis of 100-ps trajectories reveals that increasing B-site ionic radius simultaneously decreases X-A collision's activation energy (enhancing reaction rates) and decreases X-A collision's pre-exponential factor (reducing collision frequency), producing opposing kinetic effects. Such "kinetic tug-of-war" explains why an intermediate-sized cation yields maximal thermal stability by optimally balancing reactivity and collision dissipation. A similarly sized reactive cation promotes additional hydrogen-transfer pathways causing accelerating decomposition. Our findings link atomistic kinetics to macroscopic stability, informing next-generation EMs design.

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