Datasets from "Electrostatic Embedding of Machine Learning Potentials"

Name: Datasets from "Electrostatic Embedding of Machine Learning Potentials"
Creator: Zinovjev, Kirill
Keywords: Machine Learning, QM7, Electrostatic Embedding, QM/MM

Zinovjev, Kirill

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Dataset . 2022

License: CC BY

Data sources: Datacite

ZENODO

Dataset . 2022

License: CC BY

Data sources: Datacite

Datasets from "Electrostatic Embedding of Machine Learning Potentials"

Research datakeyboard_double_arrow_right Dataset 04 Sep 2022Publisher:Zenodo

Authors: Zinovjev, Kirill;

doi: 10.5281/zenodo.7048724 , 10.5281/zenodo.7051785

Datasets from "Electrostatic Embedding of Machine Learning Potentials"

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Abstract

Data required to reproduce results in "Electrostatic Embedding of Machine Learning Potentials" article. See https://github.com/emedio/embedding for details. QM7_B3LYP_cc-pVTZ.tgz - outputs of single point B3LYP/cc-pVTZ calculations of structures in QM7 dataset with ORCA 5. Include molecular dipolar polarizabilities. QM7_B3LYP_cc-pVTZ_horton.tgz - MBIS partitioning of the B3LYP/cc-pVTZ densities with Horton 2.1.0. mpro_xyz.tgz - coordinates of the ligand and surrounding point charges from 100 snapshots of SARS-CoV-2 Mpro complex with PF-00835231. mpro_*.tgz - DFT and semiempirical single point calculations with ORCA 5 for the coordinates from mpro_xyz.tgz, in vacuo and in presence of point charges. mlmm.mat - learned parameters and SOAP feature vectors of reference atomic environments

Related Organizations

University of Valencia
Spain

Keywords

Machine Learning, QM7, Electrostatic Embedding, QM/MM

2 Research products, page 1 of 1

Datasets from "Electrostatic Embedding of Machine Learning potentials" article
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