Materials Informatics for Catalyst Stability and Functionality
Steven Torrisi, Harvard University
After a brief survey of my overall Ph.D. work, which encompasses fields of machine learning, computational materials science, and high-throughput computing, I will give a detailed look at my practicum project at Lawrence Berkeley National Laboratory, which focused on the discovery of two-dimensional photoelectrocatalysts. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO2-reduction photoelectrocatalysts, we screen for candidate monolayers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO2 reduction, determine exciton binding energies, and compute surface reactivity. We find visible light absorption for SiAs, ZnTe, and ZnSe monolayers, and that due to a lack of binding, CO selectivity is possible. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO2 photoreduction candidates.
Abstract Author(s): Steven Torrisi, Kristin Persson, Santosh Suram, Linda Hung, Efthimios Kaxiras, Boris Kozinsky