The spontaneous decomposition of organic molecules during synthesis, handling, or storage causes significant safety issues in the field of energetic materials. Besides thermal activation, recent studies suggest that intramolecular deformations, such as those induced by shock waves, significantly influence chemical reactivity and may alter decomposition mechanisms.
Molecular-level studies of these phenomena present significant challenges because they require both quantum-level accuracy for bond breaking and formation and the inclusion of condensed phase effect.
To bridge this gap, we propose the development and application of machine learning-based interatomic potentials (MLIPs),
In particular, we aim to significantly advance methodologies for building reactive structural datasets, specifically tailored to complex thermal and mechanochemical reactions with multiple decomposition pathways. Leveraging these improved datasets, we will develop MLIPs to study molecular decomposition under varying temperature and pressure conditions. Besides the safety concerns inherent to energetic molecules, the tools and knowledge developed during the project are expected to be of great value to the mechanochemistry community who currently lacks a molecular-level understanding of transformations in mechanochemical systems.