Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials

Input, output, and trajectories of molecular dynamics (MD) simulations of water, acetonitrile, and methanol. Simulations were driven by many-body machine learning (mbML) potentials including explicit 1-, 2-, and 3-body contributions. GDML, GAP, and SchNet models are provided in a separate repository. All simulations were performed in the atomic simulation environment (ASE). Analyses including radial distribution function (rdf) curves are provided here. <strong>Manifest</strong> The following simulations are included in this repository for each solvent. 1 ps hexamer NVE MD simulation driven by MP2/def2-TZVP (in ORCA v4.2.0), mbGDML, mbGAP, mbSchNet, and GFN2-xTB started with the same positions and velocities. Velocities were initialized at 298.15 K with a Maxwell-Boltzmann distribution. Periodic NVT MD simulation at 298.15 K for 10 or 30 ps with a 1 fs time step driven by mbGDML. These simulations contained 58 or <strong>137</strong> water molecules, <strong>67</strong> or 122 acetonitrile molecules, <strong>61</strong> methanol molecules. Systems that are not bolded were used for testing purposes.