Thomas James Mustard

Abstract Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data.

The study of structural properties of amorphous structures is complicated by the lack of long-range order and necessitates the use of both cutting-edge computer modeling and experimental techniques. With regards to the computer modeling, many questions on convergence arise when trying to assess the accuracy of a simulated system. What cell size maximizes the accuracy while remaining computationally efficient?