Portrait of Michel Côté

Michel Côté

Associate Academic Member
Université de Montréal
Research Topics
Computational Physics
Condensed Matter Physics
Molecular Modeling

Current Students

PhD - Université de Montréal
Postdoctorate - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal

Publications

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes
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.
Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles
Ao Wang
Junwen Yin
Félix Antoine Goudreault
Olle Hellman
Samuel Poncé
Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current fi… (see more)rst-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron-phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al.
Large scale Raman spectrum calculations in defective 2D materials using deep learning
We introduce a machine learning prediction workflow to study the impact of defects on the Raman response of 2D materials. By combining the u… (see more)se of machine-learned interatomic potentials, the Raman-active Γ-weighted density of states method and splitting configurations in independant patches, we are able to reach simulation sizes in the tens of thousands of atoms, with diagonalization now being the main bottleneck of the simulation. We apply the method to two systems, isotopic graphene and defective hexagonal boron nitride, and compare our predicted Raman response to experimental results, with good agreement. Our method opens up many possibilities for future studies of Raman response in solid-state physics.