Fokker-Planck score learning: efficient free-energy estimation under periodic boundary conditions

Accurate free-energy estimation is essential in molecular simulation, yet the periodic boundary conditions (PBC) commonly used in computer simulations have rarely been explicitly exploited. Equilibrium methods such as umbrella sampling, metadynamics, and adaptive biasing force require extensive samp...

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Hauptverfasser: Nagel, Daniel (VerfasserIn) , Bereau, Tristan (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 13 November 2025
In: The journal of physical chemistry. B, Biophysics, biomaterials, liquids, and soft matter
Year: 2025, Jahrgang: 129, Heft: 45, Pages: 11780-11790
ISSN:1520-5207
DOI:10.1021/acs.jpcb.5c04579
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1021/acs.jpcb.5c04579
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Verfasserangaben:Daniel Nagel and Tristan Bereau
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Zusammenfassung:Accurate free-energy estimation is essential in molecular simulation, yet the periodic boundary conditions (PBC) commonly used in computer simulations have rarely been explicitly exploited. Equilibrium methods such as umbrella sampling, metadynamics, and adaptive biasing force require extensive sampling, while nonequilibrium pulling with Jarzynski’s equality suffers from poor convergence due to exponential averaging. Here, we introduce a physics-informed, score-based diffusion framework: by mapping PBC simulations onto a Brownian particle in a periodic potential, we derive the Fokker-Planck steady-state score that directly encodes free-energy gradients. A neural network is trained on nonequilibrium trajectories to learn this score, providing a principled scheme to efficiently reconstruct the potential of mean force. On benchmark periodic potentials and small-molecule membrane permeation, our method is up to 1 order of magnitude more efficient than umbrella sampling.
Beschreibung:Online veröffentlicht: 30. Oktober 2025
Gesehen am 01.12.2025
Beschreibung:Online Resource
ISSN:1520-5207
DOI:10.1021/acs.jpcb.5c04579