Solvation Free Energies from Neural Thermodynamic Integration

• URL: http://arxiv.org/abs/2410.15815v1
• Date: Mon, 21 Oct 2024 09:28:46 GMT
• Title: Solvation Free Energies from Neural Thermodynamic Integration
• Authors: Bálint Máté, François Fleuret, Tristan Bereau,
• Abstract summary: We compute solvation free energies along a neural-network potential interpolating between two target Hamiltonians. We validate our method to compute solvation free energies on several benchmark systems.
• Score: 19.871787625519513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose to compute solvation free energies via thermodynamic integration along a neural-network potential interpolating between two target Hamiltonians. We use a stochastic interpolant to define an interpolation between the distributions at the level of samples and optimize a neural network potential to match the corresponding equilibrium potential at every intermediate time-step. Once the alignment between the interpolating samples and the interpolating potentials is sufficiently accurate, the free-energy difference between the two Hamiltonians can be estimated using (neural) thermodynamic integration. We validate our method to compute solvation free energies on 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.

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