(611g) Practical Applications of Machine Learning to Catalyst Design and Discovery

Heterogeneous catalysis is fundamental to chemical industry and consumes several percent of the entire global energy supply. Reducing this usage and enabling next-generation energy solutions such as direct conversion of CO2 to fuels requires the design of new catalysts with optimal activity, selectivity, and stability. Scientific computing advances have enabled electronic structure codes to aid in this design process but fundamental limitations make it unlikely that direct simulation of macroscopic catalysts will be possible. The huge design space can be reduced by recognizing similarities in materials (developing structural fingerprints) and adopting regression tools from the systems engineering or machine learning communities to provide useful surrogate models as a guide for full-accuracy calculations. I will discuss how we approach the coupled problems of database generation, calculation automation, and active learning to search for intermetallic catalysts with near-optimal properties. We distinguish these approaches from typical high throughput screens because the final calculations are functionally identical to the standard computational chemistry workflow that would be used for individual surfaces. We demonstrate the successful identification of more than 100 new candidates for the CO2 reduction reaction, and show that the final predictive models can aid in the determination of the active facet in polycrystalline catalysts.