Félix Therrien

Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study

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.

2026-04-23

OpenReview (publié)

doi.org

openreview.net

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 Hernández-García

Accurate prediction of ionic conductivity is critical for the design of highperformance solid-state electrolytes in next-generation batterie… (voir plus)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

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

Sun Sun

Alain Tchagang

Jiang Su

Samuel Huberman

Yoshua Bengio

Hongyu Guo

Alex Hernández-García

Homin Shin

OBELiX is a database of 599 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities … (voir plus)gathered from literature and curated by domain experts.

2025-12-31

Digital Discovery (publié)

doi.org

openreview.net

Adsorption energies are necessary but not sufficient to identify good catalysts

Alexander Davis

Alexandre AGM Duval

Oleksandr Voznyy

Alex Hern'andez-Garcia

David Rolnick

Félix Therrien

2025-12-04

ArXiv (prépublication)

arxiv.org

Novel method to automatize flash point detection in small volumes of liquid by computer vision using thermal images

Caroline St‐Antoine

Marie-Chloé Michaud Paradis

Pauline Gonnel

Gabrielle Foran

Félix Therrien

Arnaud Prébé

Mickaël Dollé

• Novel methodology for flash point detection of 15 to 300 μL of liquid. • Closed-cup flash point apparatus combined with thermal imagi… (voir plus)ng camera. • Use of computer vision for flash point detection. • Flash point detection at 95 % accuracy. A novel methodology to automate flash point detection of small solvent volumes has been successfully demonstrated and optimized. Flash point temperatures were measured using a closed-cup rapid flash point tester which was paired with a thermal imaging camera. The thermal imaging camara analyses the apparent temperature of the flame before, during and after the opening of the chamber. Two flash point standards and n-eicosane were used to validate the apparatus. Sample volume ranged between 15 and 300 μL which is of interest for the analysis of expensive or harmful liquids. This approach could eventually be extended to measuring flash points in gel polymer electrolytes, for which flammability testing is of significant interest for battery R&D. In this work, 462 flashpoints were collected to feed the machine learning algorithms. Convolutional neural network, support vector machine and random forest algorithms were used to determine the presence/absence of a flame. Flash points were predicted with an accuracy of 95 % and a precision of ±2 °C. Precision was found to be limited by the flash point detector rather than the analysis by computer vision. Other factors such as humidity (22 % to 55 %), atmospheric pressure (between 99.6 to 102.0 kPa) and volume of solvent were found to have little to no influence on flash point detection. The flash point temperatures tested in this study are limited to a range between 50 °C and 176 °C. Regression algorithms were employed to estimate the flash point temperature based on a single measurement presenting an improvement as accurate flash point detection traditionally requires several measurements.

2025-04-22

Measurement (publié)

doi.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