Solvation free energies from neural thermodynamic integration
We present a method for computing free-energy differences using thermodynamic integration with a neural network potential that interpolates between two target Hamiltonians. The interpolation is defined at the sample distribution level, and the neural network potential is optimized to match the corre...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2025
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| In: |
The journal of chemical physics
Year: 2025, Jahrgang: 162, Heft: 12, Pages: 124107-1 - 124107-9 |
| ISSN: | 1089-7690 |
| DOI: | 10.1063/5.0251736 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1063/5.0251736
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| Verfasserangaben: | Bálint Máté, François Fleuret, Tristan Bereau |
| Zusammenfassung: | We present a method for computing free-energy differences using thermodynamic integration with a neural network potential that interpolates between two target Hamiltonians. The interpolation is defined at the sample distribution level, and the neural network potential is optimized to match the corresponding equilibrium potential at every intermediate time step. Once the interpolating potentials and samples are well-aligned, the free-energy difference can be estimated using (neural) thermodynamic integration. To target molecular systems, we simultaneously couple Lennard-Jones and electrostatic interactions and model the rigid-body rotation of molecules. We report accurate results for several benchmark systems: a Lennard-Jones particle in a Lennard-Jones fluid, as well as the insertion of both water and methane solutes in a water solvent at atomistic resolution using a simple three-body neural-network potential. |
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| Beschreibung: | Online veröffentlicht: 25. März 2025 Gesehen am 15.10.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 1089-7690 |
| DOI: | 10.1063/5.0251736 |