Accelerated Predictions of Charge Density Evolution in MD simulations Using Machine Learning

Reactive force field-based molecular dynamics simulations can be utilized to build an understanding of the effects of a salt brine on the corrosion-based processes experienced by an underlying metallic substrate. However, due to the time and memory intensive nature of performing these simulations, forecasting the long-term behavior at the interface is a challenging task. Therefore, a machine learning-based protocol to minimize the computational cost associated with performing these simulations is desirable. In our study, we compare two versatile model architectures – Feed Forward Neural Networks (FFNN) and Long Short Term Memory (LSTM) networks in terms of their accuracy in forecasting the atomic charge density. These protocols accelerate the predictions of various properties from reactive force field simulations, thereby serving as a valuable tool for the development of reactive, charge-dependent, machine learned interatomic force fields for classical molecular dynamics. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. SAND2023-11069A