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 present two examples: accelerating the reduction of large reaction networks in thermal catalysis, and automatic identification of active site motifs in intermetallic electrochemical catalysis. Finally, I will discuss ongoing work to enable on-line/active-learning processes to automatically discover new intermetallics of interest to guide the experimental discovery process.