Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study

• URL: http://arxiv.org/abs/2510.02142v1
• Date: Thu, 02 Oct 2025 15:49:39 GMT
• Title: Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
• Authors: Lena Podina, Christina Humer, Alexandre Duval, Victor Schmidt, Ali Ramlaoui, Shahana Chatterjee, Yoshua Bengio, Alex Hernandez-Garcia, David Rolnick, Félix Therrien,
• Abstract summary: Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen.<n>We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and energy to design crystal surfaces that act as efficient catalysts.<n>We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst.
• Score: 73.49945279707323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst. In future work, we aim to extend this approach to the oxygen evolution reaction, where current optimal catalysts are expensive metal oxides, and open the search space to discover new materials. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.

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