ArticleJournal of chemical theory and computation2026
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
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Authors and funding
2 authors.
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Abstract
Coarse-grained (CG) models provide an effective route to reduce the complexity of molecular simulations, but conventional approaches depend heavily on long, all-atom molecular dynamics trajectories to adequately sample the configurational space. This data dependence limits accuracy and generalizability, as unvisited configurations remain excluded from the resulting CG models. We introduce a fully data-free, generative framework for coarse-graining that directly targets the all-atom Boltzmann distribution. The model defines a structured latent space comprising
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Registered trials
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