Portrait of Félix Therrien

Félix Therrien

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

Publications

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes
Dounia Shaaban Kabakibo
Félix Therrien
Yoshua Bengio
Michel Côté
Hongyu Guo
Homin Shin
Alex Hernandez-Garcia
Accurate prediction of ionic conductivity is critical for the design of highperformance solid-state electrolytes in next-generation batterie… (see more)s. We benchmark molecular dynamics (MD) approaches for computing ionic conductivity in 21 lithium solid electrolytes for which experimental ionic conductivity has been previously reported in the literature. Specifically, we compare simulations driven by density functional theory (DFT) and by universal machine-learning interatomic potentials (uMLIPs), namely a MACE foundation model. Our results suggest comparable performance between DFT and MACE, with MACE requiring only a fraction of the computational cost. The framework developed here is designed to enable systematic comparisons with additional uMLIPs and fine-tuned models in future work.
2026-03-01
AI4Mat @ International Conference on Learning Representations (poster)
openreview.net
openreview.net
Adsorption energies are necessary but not sufficient to identify good catalysts
Shahana Chatterjee
Alexander Davis
Lena Podina
Divya Sharma
Yoshua Bengio
Alexandre AGM Duval
Oleksandr Voznyy
Alex Hern'andez-Garcia
David Rolnick
Félix Therrien
2025-12-04
ArXiv (preprint)
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… (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.
2025-09-19
NeurIPS.cc/2025/Workshop/AI4Mat (poster)
doi.org
openreview.net
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…
2025-03-02
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
Leah Wairimu Mungai
Alex Hernandez-Garcia
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Homin Shin
OBELiX is a database of 599 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities … (see more)gathered from literature and curated by domain experts.
2025-02-19
arXiv (preprint)
doi.org
openreview.net