Please note: This PhD seminar will take place in DC 2314.
Lena Podina, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Mohammad Kohandel, Ali Ghodsi
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despite fluctuations in sources such as wind and solar. Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen. However, the development of affordable and high-performance catalysts for this process remains a significant challenge.
We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and adsorption energy to design crystal surfaces that act as efficient catalysts. We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), key reactions in HES, for which we successfully identified platinum (for HER) and ruthenium oxide (for OER) as the most efficient known catalysts. In future work, we aim to open the search space to discover new materials, in particular for OER, where current optimal catalysts are expensive metal oxides. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.