Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
Rare events are central to the evolution of complex many-body systems, characterized as key transitional configurations on the free energy surface (FES). Conventional methods require adequate sampling of rare event transitions to obtain the FES, which is computationally very demanding. Here we introduce the variational free energy surface (VaFES), a dataset-free framework that directly models FESs using tractable-density generative models. Rare events can then be immediately identified from the FES with their configurations generated directly via one-shot sampling of generative models. By extending a coarse-grained collective variable (CV) into its reversible equivalent, VaFES constructs ...
| Authors | Shuo-Hui Li, Chen Chen, Yao-Wen Zhang et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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