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 t...
Anton Z. Ni, Joonho Lee
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