Data-driven Computational Materials Design for Sustainable Energy Technologies

Biosketch: Dr. Robert Wexler is a postdoctoral fellow at the Princeton University under the mentorship of Professor Emily Carter. In his short time in Professor Carter’s laboratory, Dr. Wexler discovered an optimal chemical composition for kesterite-based solar cell absorbers, and he has constructed a machine-learned model for oxygen vacancy formation energies that accurately reproduces state-of-the-art quantum mechanics calculations for a diverse set of ABO3 perovskites. This model enables high-throughput screening for any application that requires the precise control of oxygen vacancy concentrations, e.g., oxide catalysts and ferroelectrics. Dr. Wexler earned his PhD at the University of Pennsylvania with Professor Andrew Rappe, where his theoretical research focused on heterogeneous catalysis and crystal structure prediction, and his thesis was titled “Toward Realistic Modeling of Catalytic Surfaces: From First Principles to Machine Learning”. Dr. Wexler earned his BS in Chemistry from the Pennoni Honors College at Drexel University.

Overall, Dr. Wexler’s research interests are focused on theoretical innovation for renewable energy and environmental applications, with an emphasis on the development of computational methods for the more realistic modeling of interfacial phenomena in heterogeneous electrocatalysis, solar energy conversion, and ferroelectric environmental energy harvesting. He is driven by the prospects of using quantum mechanics calculations, Monte Carlo and molecular dynamics simulations, and data-intensive methodologies as a synergistic approach for developing a fundamental understanding of complex materials systems, discovering relationships between their structure and function, and identifying promising routes for materials design.