Datasets from "Electrostatic Embedding of Machine Learning Potentials"

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 molecula...

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Bibliographic Details
Main Author: Kirill Zinovjev
Format: Dataset
Language:unknown
Published: 2022
Subjects:
QM/MM
Machine Learning
QM7
Electrostatic Embedding
Orca
Online Access:https://zenodo.org/record/7051785
https://doi.org/10.5281/zenodo.7051785
id ftzenodo:oai:zenodo.org:7051785
record_format openpolar
spelling ftzenodo:oai:zenodo.org:7051785 2023-05-15T17:53:24+02:00 Datasets from "Electrostatic Embedding of Machine Learning Potentials" Kirill Zinovjev 2022-09-04 https://zenodo.org/record/7051785 https://doi.org/10.5281/zenodo.7051785 unknown doi:10.5281/zenodo.7048724 https://zenodo.org/record/7051785 https://doi.org/10.5281/zenodo.7051785 oai:zenodo.org:7051785 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode QM/MM Machine Learning QM7 Electrostatic Embedding info:eu-repo/semantics/other dataset 2022 ftzenodo https://doi.org/10.5281/zenodo.705178510.5281/zenodo.7048724 2023-03-11T03:14:01Z 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 Dataset Orca Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
topic QM/MM
Machine Learning
QM7
Electrostatic Embedding
spellingShingle QM/MM
Machine Learning
QM7
Electrostatic Embedding
Kirill Zinovjev
Datasets from "Electrostatic Embedding of Machine Learning Potentials"
topic_facet QM/MM
Machine Learning
QM7
Electrostatic Embedding
description 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
format Dataset
author Kirill Zinovjev
author_facet Kirill Zinovjev
author_sort Kirill Zinovjev
title Datasets from "Electrostatic Embedding of Machine Learning Potentials"
title_short Datasets from "Electrostatic Embedding of Machine Learning Potentials"
title_full Datasets from "Electrostatic Embedding of Machine Learning Potentials"
title_fullStr Datasets from "Electrostatic Embedding of Machine Learning Potentials"
title_full_unstemmed Datasets from "Electrostatic Embedding of Machine Learning Potentials"
title_sort datasets from "electrostatic embedding of machine learning potentials"
publishDate 2022
url https://zenodo.org/record/7051785
https://doi.org/10.5281/zenodo.7051785
genre Orca
genre_facet Orca
op_relation doi:10.5281/zenodo.7048724
https://zenodo.org/record/7051785
https://doi.org/10.5281/zenodo.7051785
oai:zenodo.org:7051785
op_rights info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.705178510.5281/zenodo.7048724
_version_ 1766161118680580096

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