Statistical Learning of Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics*

Complex chemical processes, such as the decomposition of energetic materials or the adsorption of water on titania nanoparticles, are typically studied using large-scale molecular dynamics (MD) simulations. These computations may involve thousands of atoms forming hundreds of molecular species and undergoing thousands of reactions. It is natural to wonder whether this wealth of data can be utilized to build more efficient, interpretable, and predictive models. In this talk, we will use techniques from statistical learning to develop a framework for constructing kinetic Monte Carlo (KMC) models from MD data. We will show that our KMC models can not only extrapolate the behavior of the chemical system by as much as an order of magnitude in time, but can also be used to study the dynamics of entirely different chemical trajectories with a high degree of fidelity. Importantly, our KMC models require only minutes to simulate systems and timescales that typically require weeks using MD. The ability of our trained KMC models to quickly extrapolate to different chemistries suggests a path forward for accelerating the simulation of novel materials.