Catalysis Informatics

Electrocatalysis is a critical element in mitigating climate change by using renewable energy to drive reactions into sustainable fuels and chemicals. Electrocatalytic reaction depends on multiple parameters, which challenge performance metrics i.e. poor selectivity, bad energy efficiency, and the catalyst materials drop in performance over time. The lack of standards towards designing and setting up experiments challenges the creation of new usable knowledge and pushing the field forward. 

This project sets out to address this by developing pareto-optimality guided generative model for electrocatalytic CO2 reduction, using a) data streams from scientific research literature, experiments and atomistic simulations, b) hypothesis-driven statistical design-of-experiments to develop the pareto algorithms. The project will predict new and under-explored optimal experiments and conditions, balanced data efficiency, and have expert-in-the-loop with domain-knowledge to improve the model. The potential of this project is to initiate a catalyst informatics platform by offering a new way of developing and improving catalysis, thus, this project is the seed needed for further collaborative funding between the PIs.