FURI | Spring 2024

Developing a Theoretical Understanding of Water-Network Formation On The Surface of Metal Oxides

Prior work shows that the arrangement of water at a metal-oxide surface dominates the catalytic and adsorption properties of the material, but the determination of this water structure is difficult. This project will utilize newly developed methodologies that combine Density Functional Theory (DFT) and machine learning to accelerate this determination on example metal-oxides  alpha-Al2O3 and alpha-Fe2O3. The goal of this project is to develop a better understanding of the compositional and structural effects of metal-oxide water-network formation while demonstrating the effectiveness of these new methodologies.