Estimating Solvation Free Energies with Boltzmann Generators
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918421000617984 |
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| author | Schebek, Maximilian Froböse, Nikolas M. Keller, Bettina G. Rogal, Jutta |
| author_facet | Schebek, Maximilian Froböse, Nikolas M. Keller, Bettina G. Rogal, Jutta |
| contents | Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space distributions of a solute in the gas phase and in solution. Here, we introduce a computational framework based on normalizing flows that directly maps solvent configurations between solutes of different sizes, and compare the accuracy and efficiency to conventional free energy estimates. For a Lennard-Jones solvent, we demonstrate that this approach yields acceptable accuracy in estimating free energy differences for challenging transformations, such as solute growth or increased solute-solute separation, which typically demand multiple intermediate simulation steps along the transformation. Analysis of radial distribution functions indicates that the flow generates physically meaningful solvent rearrangements, substantially enhancing configurational overlap between states in configuration space. These results suggest flow-based models as a promising alternative to traditional free energy estimation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Solvation Free Energies with Boltzmann Generators Schebek, Maximilian Froböse, Nikolas M. Keller, Bettina G. Rogal, Jutta Statistical Mechanics Machine Learning Computational Physics Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space distributions of a solute in the gas phase and in solution. Here, we introduce a computational framework based on normalizing flows that directly maps solvent configurations between solutes of different sizes, and compare the accuracy and efficiency to conventional free energy estimates. For a Lennard-Jones solvent, we demonstrate that this approach yields acceptable accuracy in estimating free energy differences for challenging transformations, such as solute growth or increased solute-solute separation, which typically demand multiple intermediate simulation steps along the transformation. Analysis of radial distribution functions indicates that the flow generates physically meaningful solvent rearrangements, substantially enhancing configurational overlap between states in configuration space. These results suggest flow-based models as a promising alternative to traditional free energy estimation methods. |
| title | Estimating Solvation Free Energies with Boltzmann Generators |
| topic | Statistical Mechanics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2512.18147 |