Gordon, Adrian2024-07-242024-07-242024-05https://hdl.handle.net/11299/264309University of Minnesota Ph.D. dissertation. May 2024. Major: Chemistry. Advisor: Jason Goodpaster. 1 computer file (PDF); viii, 90 pages.Atomistic simulations play an important role in a wide range of chemical investigations, including studies of chemical kinetics. These simulations rely on accurate energies and forces, often obtained through expensive ab initio electronic structure calculations. Recently researchers have explored the use of machine learning models to provide analytical and differentiable potential energy surfaces for use in atomistic simulations. These ML models can provide energies at a fraction of the cost of ab initio methods and are also highly accurate within the chemical space represented in the training data. In this work, we explore methods for data sampling techniques for training datasets used to train ML potentials, specifically to calculate chemical kinetics of the OH+ CH4 hydrogen abstraction reaction. In addition, combined ML and molecular mechanics methods for condensed phase reactions is discussed.enChemical kineticsChemical reactionsComputational chemistryMachine learning potentialsThesis or Dissertation