I am a PhD student in Mie Andersens group working on applying machine learning techniques to yield insights into the relationship between structural and catalytic properties of materials.

Prior to my PhD I was also a master’s student in Mie's group where my work focused on using an interpretable machine learning model to identify correlations between the physical features of catalytic nanoclusters and the binding energies of reaction intermediates. In the course of this project, I also constructed a dataset of catalytic nanocluster structures and DFT evaluated binding energies for model training.

Currently I am building upon the work done in my master’s thesis by expanding the space of physical features included in the model. I am also further optimizing the workflow for constructing training datasets of binding energies, which I will then apply to investigate structure-property relationships in a more diverse set of materials.