Solvating machine-learning interatomic potentials for solution-phase chemistry
Most machine-learning interatomic potentials (MLIPs) are trained on gas-phase data, which limits their use for chemistry in solution. Here, we introduce AnySolv, a transferable implicit solvation potential that provides solvent-dependent energy and force corrections to existing gas-phase potentials without retraining them. It is trained on 1.4 million solvation energies and forces computed with density functional theory (DFT) and SMD in 36 solvents, spanning charged, open-shell, and transition-state structures. Because solvents are represented by continuous physical descriptors, AnySolv supports 179 solvents, including those absent from training. On external benchmarks, AnySolv reproduces...
| Authors | Anton Z. Ni, Joonho Lee |
|---|---|
| Primary category | physics.chem-ph |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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