Machine-Learning a Solution for Reactive Atomistic Simulations of Energetic Materials

Many of the safety and performance-related properties of energetic materials (EM) are related to complex condensed phase chemistry at extreme P,T conditions eluding direct experimental investigation. Atomistic simulations can play a vital role in generating insight into EM chemistry, but they rely critically on the availability of suitable interatomic potentials (force fields). The ChIMES machine learning approach enables generation of interatomic potentials for condensed phase reacting systems, with accuracy similar to Kohn-Sham density functional theory through its unique, highly flexible orthogonal basis set of interaction functions and systematically improvable many-body expansion of interatomic interactions. ChIMES has been successfully applied to a variety of systems including simple model energetic materials, both as a correction for simpler quantum theory and as a stand-alone interatomic potential. In this perspective, the successes and challenges of applying the ChIMES approach to the reactive molecular dynamics of energetic materials are outlined. Our machine-learned approach is general and can be applied to a variety of different application areas where atomic-level calculations can be used to help guide and elucidate experiments.

Automated summary

This article introduces the application of the ChIMES machine learning approach in atomic-level calculations of energetic materials, providing insight into the chemistry of EM through the generation of interatomic potentials for condensed phase reacting systems.