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physics.chem-ph2026★ 1 cited
Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
Côme Cattin, Thomas Plé, Olivier Adjoua +3
We present a distilled multi-time-step (DMTS) strategy to accelerate molecular dynamics simulations using foundation neural network models. DMTS uses a dual-level neural network wh…
physics.chem-ph2025
The Q-AMOEBA (CF) Polarizable Potential
Nastasia Mauger, Thomas Plé, Louis Lagardère +2
We present Q-AMOEBA (CF), an enhanced version of the Q-AMOEBA polarizable model that integrates a geometry-dependent charge flux (CF) term while designed for an explicit treatment…
physics.chem-ph2024
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
Thomas Plé, Olivier Adjoua, Louis Lagardère +1
Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-i…