Author Description

In this thesis, we develop ML models to address some of the challenges in computational catalysis, while avoiding some of the pitfalls of such approaches. We introduce the thesis by providing a wide discussion about the current state of ML applied to core challenges in catalysis and the untapped potential in these areas. In Chapter 2, we develop a linear and interpretable model to fuse thermochemical quantities of interest (QoI) from different fidelities of density functional theory (DFT) with chemical accuracy. We show that subgraph frequencies of molecular graphs, more commonly referred to as group additivity, provide a natural framework for such a task. In Chapter 3, we utilize the framework of subgraph frequencies to predict the error in enthalpies of formation of 2000 molecules calculated using two different functionals commonly used in heterogeneous catalysis. We do this by building a database of the enthalpies from the NIST database and comparing it against the calculated values. Our model reduce error in these values by an order of magnitude. Having a linear model with interpretable features enables us to reason about limitations of the model, as well as gives an intuitive indication of what the model parameters mean.