Computational Heterogeneous Electrochemistry – From Quantum Mechanics to Machine Learning

Abstract

Because of coulomb interactions and complex surface morphologies, rigorous methods for heterogeneous electrochemical catalysis were not well-established. Thus, for different types of electrochemical systems, a specific strategy must be adapted. In this thesis, we first used the cluster model to study the chemistry on a 1D chain of MoS2 edges. Then, a rigorous grand canonical potential kinetics (GCP-K) method was developed for general crystalline systems. Starting from quantum mechanical calculations, the method gave rise to a different picture from the traditional description given by the Butler-Volmer kinetics. Next, we studied the chemical selectivity of CO2 reduction on polycrystalline copper nanoparticles. Because of the complexity of the reaction sites, we combined the reactive force field, density functional theory, and machine learning method to predict the reactive sites on 20,000 sites on a roughly 200,000-atom nanoparticle. Such a strategy opens up new way to understand chemistries on a much wider range of complex structures that were impossible to study theoretically. Lastly, we formulated a machine learning force field strategy using atomic energies for amorphous systems. We have shown that such a method can be used to reproduce quantum mechanical accuracies for molecular dynamics. This method will enable the accurate study of the dynamics of heterogeneous systems during electrochemical reactions. In summary, we have developed quantum chemical methods and machine learning strategies to reformulate rigorous ways to study a wide range of heterogeneous electrochemical catalysts.