4 papers
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…
Probing the partition function for temperature-dependent potentials with nested sampling
Lune Maillard, Philippe Depondt, Fabio Finocchi +4
Thermodynamic properties can be in principle derived from the partition function, which, in many-atom systems, is hard to evaluate as it involves a sum on the accessible microscopi…
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…
Velocity Jumps for Molecular Dynamics
Nicolaï Gouraud, Louis Lagardère, Olivier Adjoua +3
We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical La…