Tutorials
Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling
arXiv:2610.05998v1 Announce Type: new Abstract: Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimatin
arXiv:2610.05998v1 Announce Type: new Abstract: Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimating ensemble properties and transition dynamics that occur on much longer timescales. We introduce Langevin Flow Maps, which extend machine-learned force-fields to additionally learn the stochastic Langevin integrator. We show that Langevin Flow Maps enable large-timestep molecular dynamics and recover accurate dynamical properties of the system, while running an order of magnitude faster than current machine-learned force fields. Further, by training on a diverse molecular dataset, we demonstrate a path towards transferable Langevin Flow Maps.
Related
- Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
- Ito maps for any-step SDEs
- UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
- Latent Process Generator Matching
Source: arXiv cs.LG | 2026-10-06