mlsanityData to 7 Oct 2026

Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model

arXiv 2312.09404 ↗First posted 14 Dec 2023cs.LG cond-mat.stat-mech physics.chem-phcited by 1

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Abstract (arXiv)

Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to high intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While methods like temperature-accelerated molecular dynamics (TAMD) can adopt many CVs in a simulation, unbiasing the simulation requires accurate modeling of a high-dimensional CV probability distribution, which is challenging for traditional density estimation techniques. Here we propose an unbiasing method based on the score-based diffusion model, a deep generative learning method that excels in density estimation across complex data landscapes. We test the score-based diffusion unbiasing method on TAMD simulations. The results demonstrate that this unbiasing approach significantly outperforms traditional unbiasing methods, and can generate accurate unbiased conformational ensembles for simulations with a number of CVs higher than usual ranges.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 83 references that are in the map. First posted on arXiv 14 Dec 2023.

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14 of 83 references.

References · 14

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2 more
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  2. 2017Unfolding Hidden Barriers by Active Enhanced Samplingcited by 6

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniquescited by 2

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  1. 2026Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
  2. 2025Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance
  3. 2024Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations
  4. 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
  5. 2026Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
  6. 2026Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
  7. 2026Leveraging generative models to assist Monte Carlo sampling
  8. 2024Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models