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

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. <strong>Changelog</strong> 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!