Portrait de Christina Humer

Christina Humer

Collaborateur·rice de recherche - Johannes Kepler University
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Apprentissage en ligne
Apprentissage multimodal
Site web
Google Scholar
GitHub

Publications

Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Lena Podina
Christina Humer
Alexandre AGM Duval
Victor Schmidt
Ali Ramlaoui
Shahana Chatterjee
Yoshua Bengio
Alex Hernandez-Garcia
David Rolnick
Félix Therrien
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despit… (voir plus)e 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.
2025-10-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Lena Podina
Christina Humer
Alexandre AGM Duval
Victor Schmidt
Ali Ramlaoui
Shahana Chatterjee
Yoshua Bengio
Alex Hernandez-Garcia
David Rolnick
Félix Therrien
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despit… (voir plus)e 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.
2025-10-02
ArXiv (prépublication)
arxiv.org
arxiv.org