Portrait de Félix Therrien

Félix Therrien

felix.therrien@mila.quebec
Scientifique en apprentissage automatique appliqué, Recherche appliquée en apprentissage automatique

Publications

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
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… (voir plus) 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 (prépublication)
arxiv.org
arxiv.org
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… (voir plus) 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 (prépublication)
doi.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 (prépublication)
arxiv.org
arxiv.org