Exploring complex reaction networks using neural network-based molecular dynamics simulation

Ab initio molecular dynamics (AIMD) is an established method to reveal the reactive dynamics of complex systems. However, the computational cost of AIMD restricts the explorable length and time scales to a great extent. Here, we develop a fundamentally different approach using molecular dynamics simulations powered by a neural network potential to investigate complex reaction networks. This potential is trained via a workflow combining AIMD and interactive molecular dynamics in virtual reality (VRMD) to accelerate the sampling of a rare reactive process. The capability of the methodology is demonstrated by achieving a panoramic visualization of the complex reaction networks for decomposition of a novel high explosive (ICM-102), without any predefined reaction coordinates. The study leads to the discovery of new pathways that would be difficult to uncover employing established methods. These results highlight the power of neural network-based molecular dynamics simulations for exploration of complex reaction mechanisms under extreme conditions at the ab initio level, pushing the limit of theoretical and computational chemistry towards the realism and fidelity of experiments.