Katzberger P., Pultar F., and Riniker S., J. Chem. Theory Comput. 2025 DOI: doi:10.1021/acs.jctc.5c00728
The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model by transferring knowledge obtained from classical interactions to QM, emulating a QM/MM setup with electrostatic embedding and a nonpolarizable MM solvent. This has the profound advantages that neither QM/MM reference calculations nor experimental data are required for training and that the obtained graph neural network (GNN)-based implicit solvent model (termed QM-GNNIS) is compatible with any functional and basis set. QM-GNNIS is currently applicable to small organic molecules and describes 39 different organic solvents. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models paired with static QM calculations.
# Install environment.
conda env create -f environment.yml
conda activate QMGNNIS
# Install repo after cloning from github
pip install .
Then install the GNNIS model following the instructions of the GNNIS repository.
All necessary packages for the GNNIS model are allready included in the environment.yml file.
To run the workflows you also need to install the ORCA quantum chemistry software.
An example on how to use the tools are provided in the demo.ipynb notebook.
This section is intended to provide a step-by-step guide to reproduce the results of the paper.
The minimizations for the 22 molecular balances were performed using the submission script detailed in the submission_scripts folder. The results were analysed using the analyse_Molecular_Balances_pub.ipynb notebook.
The minimizations for the two compounds were performed using the the submit_minimisations_B3LYP_TZVP.sh script. The results were analysed using the analyse_Jtot_and_IR.ipynb notebook.