Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909821508255744 |
|---|---|
| author | Podina, Lena Humer, Christina Duval, Alexandre Schmidt, Victor Ramlaoui, Ali Chatterjee, Shahana Bengio, Yoshua Hernandez-Garcia, Alex Rolnick, David Therrien, Félix |
| author_facet | Podina, Lena Humer, Christina Duval, Alexandre Schmidt, Victor Ramlaoui, Ali Chatterjee, Shahana Bengio, Yoshua Hernandez-Garcia, Alex Rolnick, David Therrien, Félix |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02142 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study Podina, Lena Humer, Christina Duval, Alexandre Schmidt, Victor Ramlaoui, Ali Chatterjee, Shahana Bengio, Yoshua Hernandez-Garcia, Alex Rolnick, David Therrien, Félix Machine Learning Materials Science 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. |
| title | Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2510.02142 |