ArticleNature communications2025
Flow perturbation to accelerate Boltzmann sampling.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Scalable Boltzmann generators for equilibrium sampling of large-scale materials.Nature communications · 2026Article
- Flow perturbation to accelerate Boltzmann sampling.Nature communications · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Flow-based generative models have been employed for Boltzmann sampling tasks, but their application to high-dimensional systems is hindered by the significant computational cost of obtaining the Jacobian of the flow. We introduce a flow perturbation method that bypasses this bottleneck by injecting stochastic perturbations into the flow, delivering orders-of-magnitude speed-ups. Unlike the Hutchinson estimator, our approach is inherently unbiased in Boltzmann sampling. Notably, this method significantly accelerates Boltzmann sampling of a Chignolin mutant with all atomic Cartesian coordinates explicitly represented, while delivering more accurate results than the Hutchinson estimator.
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Registered trials
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