Portrait of Félix Therrien

Félix Therrien

felix.therrien@mila.quebec
Applied Machine Learning Scientist, Applied Machine Learning Research

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… (see more)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 (preprint)
arxiv.org
arxiv.org
A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
Ivan Grega
Félix Therrien
Abhishek Soni
Karry Ocean
Kevan Dettelbach
Ribwar Ahmadi
Mehrdad Mokhtari
Curtis P. Berlinguette
Yoshua Bengio
The electrochemical reduction of atmospheric CO…
2025-03-03
ICLR.cc/2025/Workshop/AI4MAT (poster)
doi.org
openreview.net
OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
Félix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Alex Hernandez-Garcia
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Homin Shin
Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (see more) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of
2025-02-20
ArXiv (preprint)
doi.org
arxiv.org
OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
F'elix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Alex Hern'andez-Garc'ia
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Homin Shin
Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (see more) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of
2025-02-20
ArXiv (preprint)
arxiv.org
arxiv.org
A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
Ivan Grega
F'elix Therrien
Abhishek Soni
Karry Ocean
Kevan Dettelbach
Ribwar Ahmadi
Mehrdad Mokhtari
C. Berlinguette
Yoshua Bengio
The electrochemical reduction of atmospheric CO…
2025-02-10
ArXiv (preprint)
arxiv.org
arxiv.org