Many-body machine learning models for water, acetonitrile, and methanol

• Main Authors: Maldonado, Alex M., Poltavsky, Igor, Vassilev-Galindo, Valentin, Tkatchenko, Alexandre, Keith, John A.
• Format: Dataset
• Language: English
• Published: 2022
• Subjects: Orca
• Online Access: https://zenodo.org/record/7464581; https://doi.org/10.5281/zenodo.7464581

GDML, GAP, and SchNet models trained on 1-, 2-, and 3-body energies and forces of water, acetonitrile, and methanol. Size-transferable NequIPs are trained on trimer data. Energies and forces were computed at the MP2/def2-TZVP level of theory in ORCA v4.2.0. Data sets, training scripts, and analyses of these potentials are available here. Applications of these models on molecular dynamics simulations are found here. Changelog The format is based on Keep a Changelog, and this project adheres to Semantic Versioning. [0.0.2] - 2022-12-20 Added NequIPs trained for all solvents using 1000 trimers. [0.0.1] - 2022-09-25 Initial release!