Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling
- 1Department of Mechanical Engineering
- 2Stanford University
Abstract
Atomistic simulations are often restricted by timescale limitations when systems become trapped in metastable energy basins. The transitions between these metastable states are rare events and they dictate the long-term evolution of the system. We present an importance sampling framework to accelerate the time scale of Langevin dynamics simulations. The framework uses a neural network parameterized importance function to bias the dynamics, enhancing the efficiency of rare-transition sampling while preserving relative probabilities between transition paths. We provide a rigorous mathematical formulation to recover the original transition rates between metastable states from the biased dynamics, and use a branching random walk algorithm to control the statistical variance of the estimated rates. We validate the framework on 2- and 14-dimensional problems, as well as the system of 7 Lennard-Jones discs (d = 14) model. We further apply it to two realistic materials systems: the diffusion of a Pt adatom on the Pt(001) surface (d = 387) and the migration of a vacancy in BCC \alpha-iron (d = 2055). The framework provides a scalable foundation for accelerating the simulation of rare events in atomistic systems.
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BibTeX
@misc{kim2026accelerated,
title = {{Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling}},
author = {Michael Kim and Wei Cai},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15000772/v3},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000772/v3},
note = {CC BY},
}
RIS
TY - UNPB
TI - Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling
AU - Michael Kim
AU - Wei Cai
PY - 2026
DA - 2026/10/08
PB - ChemRxiv
DO - 10.26434/chemrxiv.15000772/v3
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000772/v3
AB - Atomistic simulations are often restricted by timescale limitations when systems become trapped in metastable energy basins. The transitions between these metastable states are rare events and they dictate the long-term evolution of the system. We present an importance sampling framework to accelerate the time scale of Langevin dynamics simulations. The framework uses a neural network parameterized importance function to bias the dynamics, enhancing the efficiency of rare-transition sampling while preserving relative probabilities between transition paths. We provide a rigorous mathematical formulation to recover the original transition rates between metastable states from the biased dynamics, and use a branching random walk algorithm to control the statistical variance of the estimated rates. We validate the framework on 2- and 14-dimensional problems, as well as the system of 7 Lennard-Jones discs (d = 14) model. We further apply it to two realistic materials systems: the diffusion of a Pt adatom on the Pt(001) surface (d = 387) and the migration of a vacancy in BCC \alpha-iron (d = 2055). The framework provides a scalable foundation for accelerating the simulation of rare events in atomistic systems.
ER -