Theoretical advances in understanding and enhancing the thermostability of energetic materials
Runze Liu, Jian‐Yong Liu, Panwang Zhou
Abstract
molecular dynamics (AIMD) simulations provide detailed theoretical insights into the reaction pathways and the key intermediates during thermal decomposition in the condensed phase. Analyzing the kinetic barrier of rate-determining steps under various temperature and pressure conditions allows for a comprehensive assessment of thermal stability. Recent advances in machine learning have demonstrated their utility in constructing potential energy surfaces and predicting thermal stability for newly designed energetic materials. The machine learning-assisted high-throughput virtual screening (HTVS) methodology can accelerate the discovery of novel energetic materials with improved properties. As a result, the newly identified and synthesized energetic molecule ICM-104 revealed excellence in performance and thermostability. Theoretical approaches are pivotal in elucidating the mechanisms underlying thermal stability, enabling the prediction and design of enhanced thermal stability for emerging EMs. These insights are instrumental in accelerating the development of novel energetic materials that optimally balance performance and thermal stability.