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Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.

Weilong Chen1, Franz Görlich1, Paul Fuchs1

  • 1Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Munich 80333, Germany.

Journal of Chemical Theory and Computation
|December 24, 2025
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Summary
This summary is machine-generated.

Enhanced sampling accelerates molecular dynamics (MD) simulations by improving data generation for coarse-grained machine learning potentials (MLPs). This method overcomes limitations in traditional force matching, leading to more accurate and reliable CG-MLPs.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Statistical Mechanics

Background:

  • Coarse-grained (CG) models are essential for simulating large molecular systems and long timescales in molecular dynamics (MD).
  • Machine learning potentials (MLPs) offer accurate approximations for the potential of mean force (PMF) in CG models by capturing complex interactions.
  • Traditional training of CG MLPs via force matching requires extensive equilibrium simulations, limiting efficiency and sampling in critical transition regions.

Purpose of the Study:

  • To develop a novel strategy for training coarse-grained machine learning potentials (CG-MLPs) that overcomes limitations of traditional force matching.
  • To improve the efficiency and accuracy of CG-MLP development by enhancing data generation through enhanced sampling techniques.
  • To ensure thermodynamic consistency and accurate PMF representation in CG models.

Main Methods:

  • Employing enhanced sampling techniques to bias simulations along coarse-grained (CG) degrees of freedom for data generation.
  • Recomputing forces with respect to the unbiased potential after biased data generation to maintain thermodynamic consistency.
  • Applying the enhanced sampling for force matching strategy to benchmark systems, including the Müller-Brown potential and capped alanine.

Main Results:

  • Significantly reduced simulation time required to generate equilibrated and thermodynamically consistent data for CG-MLP training.
  • Enriched sampling in transition regions, which are typically poorly represented in standard equilibrium simulations.
  • Demonstrated notable improvements in the accuracy and reliability of the developed CG-MLPs on tested systems.

Conclusions:

  • Enhanced sampling for force matching is a viable and effective strategy for accelerating the development of accurate CG-MLPs.
  • This approach addresses key limitations of conventional training methods, enabling more efficient and comprehensive molecular simulations.
  • The findings pave the way for more reliable and predictive coarse-grained modeling in various scientific domains.