Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916997909970944 |
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| author | Diez, Juan Viguera Schreiner, Mathias Olsson, Simon |
| author_facet | Diez, Juan Viguera Schreiner, Mathias Olsson, Simon |
| contents | Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. Importantly, the method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended time scales. By expanding the accessible range of molecular motions without sacrificing atomistic detail, this approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_07589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics Diez, Juan Viguera Schreiner, Mathias Olsson, Simon Chemical Physics Machine Learning Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. Importantly, the method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended time scales. By expanding the accessible range of molecular motions without sacrificing atomistic detail, this approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics. |
| title | Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2510.07589 |