MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS

Abstract

We discuss a software package for incorporating into simulations data-driven models trained using machine learning methods. These can be used for (i) modeling dynamics and time-step integration, (ii) modeling interactions between system components, and (iii) computing quantities of interest characterizing system state. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. We discuss in this whitepaper our prototype C++ package, aims, and example usage.