2026-10-13T14:53:00Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34024674 2026-09-29T14:49:44Z category_4 category_915 category_873 category_734 category_931 portal_63 item_type_6 month_year_09_2026

Can Machine Learning Predict Solvation Effects on Energies and Geometries of Highly Charged Molecules? Dario Baum (23012343) Lucas Visscher (1487737) Amber Pausch (25137573) Biochemistry Physical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified total charges ranging subtle structural effects singly charged ones graph neural networks focused almost exclusively efficient conformational sampling aware geometry optimization implicit solvation based like screening model weakly charged molecules highly charged molecules continuum solvation models predict solvation energies realistic solvation model architectures learning models systematically investigate robust treatment previous studies molecular charge learned efficiently corresponding forces architectural components Recent machine-learning models have shown that implicit solvation based on continuum solvation models can be learned efficiently with graph neural networks. However, previous studies have focused almost exclusively on neutral or only weakly charged molecules, leaving the strongly charged regime essentially unexplored. Here, we systematically investigate which model architectures and architectural components are required to predict solvation energies and corresponding forces for molecules with total charges ranging from −5 to +5. Using reference data generated from the conductor-like screening model for realistic solvation (COSMO-RS) for water, acetonitrile, and cyclohexane, we show that an explicit and robust treatment of molecular charge is essential for reliable performance across charge states. At the same time, highly charged systems prove easier to learn than neutral and singly charged ones, whose solvation energies depend more strongly on subtle structural effects. Finally, we demonstrate that the machine-learning models enable stable solvent-aware geometry optimization and can be used for efficient conformational sampling in solution. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.jctc.6c00804.s001 https://figshare.com/articles/journal_contribution/Can_Machine_Learning_Predict_Solvation_Effects_on_Energies_and_Geometries_of_Highly_Charged_Molecules_/34024674 CC BY-NC 4.0