Datasets from "Electrostatic Embedding of Machine Learning Potentials"

<p>Data required to reproduce&nbsp;results in &quot;Electrostatic Embedding of Machine Learning Potentials&quot; <a href="https://doi.org/10.26434/chemrxiv-2022-rknwt">article</a>. See&nbsp;<a href="https://github.com/emedio/embedding">https://github.com/emedio/embedding</a>&nbsp;for details.</p> <ul> <li>QM7_B3LYP_cc-pVTZ.tgz - outputs of single point B3LYP/cc-pVTZ calculations of structures in <a href="http://quantum-machine.org/data/qm7.mat">QM7 dataset</a> with ORCA 5. Include&nbsp;molecular dipolar polarizabilities.</li> <li>QM7_B3LYP_cc-pVTZ_horton.tgz - MBIS partitioning of the&nbsp;B3LYP/cc-pVTZ densities with <a href="https://github.com/theochem/horton">Horton 2.1.0</a>.</li> <li>mpro_xyz.tgz - coordinates of the ligand&nbsp;and surrounding point charges from&nbsp;100 snapshots of SARS-CoV-2 Mpro complex with PF-00835231.</li> <li>mpro_*.tgz - DFT and semiempirical single point calculations&nbsp;with ORCA 5 for the coordinates from mpro_xyz.tgz, <em>in vacuo </em>and in presence of point charges.</li> <li>mlmm.mat - learned parameters and SOAP feature vectors of reference atomic environments</li> </ul>