6 papers
Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
Nicolaï Gouraud, Côme Cattin, Thomas Plé +3
Following our previous work (J. Phys. Chem. Lett., 2026, 17, 5, 1288-1295), we propose the DMTS-NC approach, a distilled multi-time-step (DMTS) strategy using non-conservative (NC)…
The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
Narjes Ansari, César Feniou, Nicolaï Gouraud +13
Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio mo…
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…
The velocity jump Langevin process and its splitting scheme: long time convergence and numerical accuracy
Nicolaï Gouraud, Lucas Journel, Pierre Monmarché
The Langevin dynamics is a diffusion process extensively used, in particular in molecular dynamics simulations, to sample Gibbs measures. Some alternatives based on (piecewise dete…
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…
HMC and underdamped Langevin united in the unadjusted convex smooth case
Nicolaï Gouraud, Pierre Le Bris, Adrien Majka +1
We consider a family of unadjusted generalized HMC samplers, which includes standard position HMC samplers and discretizations of the underdamped Langevin process. A detailed analy…