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This paper introduces FES-FM, a machine learning technique using reduced flow matching to accelerate the sampling of free energy surfaces in statistical physics. By training dynamical transport maps directly on collective variables rather than high-dimensional configuration spaces, it achieves faster convergence for modeling chemical reactions and conformational transitions while maintaining physical accuracy. The method represents an advancement in applying generative models to complex scientific computing problems within computational chemistry.
FES-FM: Free Energy Surface Sampling via Reduced Flow Matching
arXiv:2605.00337v2 Announce Type: replace Abstract: Sampling the distribution of collective variables (CVs) and estimating the associated free energy surface are crucial problems in statistical physics, as they underpin a better understanding of chemical reactions and conformational transitions. Traditional methods usually rely on simulations in high-dimensional configuration space and project the resulting configurations onto the CV space. To improve sampling speed, we propose FES-FM, a reduced flow matching (FM) method for free energy surface (FES) sampling. We train a dynamical transport map in the CV space, thereby enabling direct sampling of CV distributions and reconstruction of the corresponding free energy surface. For many-particle systems, we construct a prior distribution based on the Hessian at a local minimum of the potential, which ensures both rotation-translation invariance and physically meaningful configurations. We evaluate the proposed method across a variety of potential functions and collective variables, including alanine dipeptide in implicit solvent as a molecular benchmark. Comparative experiments demonstrate that our approach significantly improves sampling speed while maintaining accuracy.
Abstract:Sampling the distribution of collective variables (CVs) and estimating the associated free energy surface are crucial problems in statistical physics, as they underpin a better understanding of chemical reactions and conformational transitions. Traditional methods usually rely on simulations in high-dimensional configuration space and project the resulting configurations onto the CV space. To improve sampling speed, we propose FES-FM, a reduced flow matching (FM) method for free energy surface (FES) sampling. We train a dynamical transport map in the CV space, thereby enabling direct sampling of CV distributions and reconstruction of the corresponding free energy surface. For many-particle systems, we construct a prior distribution based on the Hessian at a local minimum of the potential, which ensures both rotation-translation invariance and physically meaningful configurations. We evaluate the proposed method across a variety of potential functions and collective variables, including alanine dipeptide in implicit solvent as a molecular benchmark. Comparative experiments demonstrate that our approach significantly improves sampling speed while maintaining accuracy.
Machine Learning (cs.LG)
arXiv:2605.00337 [cs.LG]
(or arXiv:2605.00337v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.00337 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 252 words
