Density functional theory (DFT) is the tool of choice for studying reactions on chemical surfaces. However, it is prohibitively expensive to use full-accuracy DFT to identify catalyst surface with optimal properties due to the sheer number of possible materials, facets, and active sites. The search process can be greatly accelerated by using flexible surrogate models that learn from previous DFT calculations to predict interesting materials. This process can be accelerated with deep-learning methods from the computer science community. Prof. Ulissi contributes to the AMP software package and is exploring larger models and networks for accelerated learning and uncertainty quantification using Google’s tensorflow library. Applications include both thermal and electrochemical catalysis.