Machine Learning Techniques in Reactive Atomistic Simulations

Abstract

AbstractThis chapter describes recent advances in the use of machine learning techniques in reactive atomistic simulations. In particular, it provides an overview of techniques used in training force fields with closed form potentials, developing machine-learning-based potentials, use of machine learning in accelerating the simulation process, and analytics techniques for drawing insights from simulation results. The chapter covers basic machine learning techniques, training procedures and loss functions, issues of off-line and in-lined training, and associated numerical and algorithmic issues. The chapter highlights key outstanding challenges, promising approaches, and potential future developments. While the chapter relies on reactive atomistic simulations to motivate models and methods, these are more generally applicable to other modeling paradigms for reactive flows.