High Pressure and Temperature Neural Network Reactive Force Field for Energetic Materials

Abstract

Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive as compared to electronic structure calculations and allow for simulations of millions of atoms. However, the accuracy of traditional force fields is limited by their functional forms, preventing continual refinement and improvement. Therefore, we develop a neural network based reactive interatomic potential for the prediction of the mechanical, thermal, and chemical response of energetic materials at extreme conditions for energetic materials. The training set is expanded in an automatic iterative approach and consists of various CHNO materials and their reactions under ambient and under shock loading conditions. This new potential shows improved accuracy over the current state of the art force fields for a wide range of properties such as detonation performance, decomposition product formation, and vibrational spectra under ambient and shock loading conditions.