Most of the chemical products that we consume nowadays are obtained using catalysts, which are materials used to accelerate chemical reactions and to tune the production of certain products over other (undesirable) materials. Typically, catalysts are selected from process heuristics or experimental studies, which require expensive and time-consuming experimental runs to test multiple combinations of active metals and their loadings. In general, the development of one catalyst that can improve the production of valuable fuels (e.g. methane) using renewable energy systems such as thermo-catalytic carbon dioxide (CO2) reduction often require years of experimental work and significant investments (>$500,000 CAD).
Computer aided catalyst design using first principles calculations such as Density Functional Theory (DFT) have shown potential to guide the experimental development of new catalysts that can successfully result in products with desirable properties. In our group, we conduct studies aimed at developing novel catalyst materials that can improve the performance of emerging processes. For instance, we have conducted first principles studies that have provided new bi-metal and multi-metal catalysts that can improve the performance of solid oxide electrolysis cells, enable the transformation of CO2 into valuable chemicals, and revealed the elementary reaction mechanisms that are likely to take place in critical solid-gas systems such as chemical looping combustion. Recently, we have also focused our efforts on the design of novel computational frameworks that combine machine learning algorithms, DFT analysis and experimental observations to optimally design catalyst materials that can reduce CO2 emissions.