Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution
Felix Pultar, Moritz Thürlemann, Igor Gordiy, Eva Doloszeski, Sereina Riniker
Abstract
). The performance and broad applicability of our approach are showcased by calculating the free-energy surface of alanine dipeptide, the preferred ligation states of nickel phosphine complexes, and dissociation free energies of charged pyridine and quinoline dimers. Results with this ML/MM approach show excellent agreement with experimental data and reach chemical accuracy in most cases. In contrast, free energies calculated with static DFT calculations paired with implicit solvent models or QM/MM MD simulations using cheaper semiempirical methods show up to ten times higher deviation from the experimental ground truth and sometimes even fail to reproduce qualitative trends.