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[2602.16634] Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Summary
The paper presents Enhanced Diffusion Sampling, a novel method for efficient rare event sampling and free energy calculation in molecular dynamics, utilizing diffusion models to improve accuracy and speed.
Why It Matters
This research addresses a critical challenge in molecular dynamics simulations, specifically the difficulty of sampling rare events. By introducing enhanced diffusion sampling, the authors provide a solution that significantly improves the efficiency of calculating free energy differences, which is vital for understanding biomolecular processes. This advancement could lead to more accurate simulations in fields such as drug discovery and protein folding.
Key Takeaways
- Enhanced Diffusion Sampling improves rare event sampling in molecular dynamics.
- The method utilizes diffusion models to generate unbiased thermodynamic estimators.
- Three algorithms are introduced: UmbrellaDiff, ΔG-Diff, and MetaDiff.
- The approach achieves fast and accurate estimations of equilibrium properties.
- This research closes the gap in rare-event sampling efficiency.
Statistics > Machine Learning arXiv:2602.16634 (stat) [Submitted on 18 Feb 2026] Title:Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models Authors:Yu Xie, Ludwig Winkler, Lixin Sun, Sarah Lewis, Adam E. Foster, José Jiménez Luna, Tim Hempel, Michael Gastegger, Yaoyi Chen, Iryna Zaporozhets, Cecilia Clementi, Christopher M. Bishop, Frank Noé View a PDF of the paper titled Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models, by Yu Xie and 12 other authors View PDF HTML (experimental) Abstract:The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioEmu have emerged as powerful equilibrium samplers that generate independent samples from complex molecular distributions, eliminating the cost of sampling rare transition events. However, a sampling problem remains when computing observables that rely on states which are rare in equilibrium, for example folding free energies. Here, we introduce enhanced diffusion sampling, enabling efficient exploration of rare-event regions while preserving unbiased thermodynamic estimators. The key idea is to perform quantitatively accurate steering protocols to generate biased ensembles and subsequently recover equilibrium statistics via exact reweighting. We instantiate our framework in three algorithms: Umbrell...
