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...
Main Authors: | , , , , |
---|---|
Format: | Dataset |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://zenodo.org/record/7464581 https://doi.org/10.5281/zenodo.7464581 |
id |
ftzenodo:oai:zenodo.org:7464581 |
---|---|
record_format |
openpolar |
spelling |
ftzenodo:oai:zenodo.org:7464581 2023-05-15T17:53:37+02:00 Many-body machine learning models for water, acetonitrile, and methanol Maldonado, Alex M. Poltavsky, Igor Vassilev-Galindo, Valentin Tkatchenko, Alexandre Keith, John A. 2022-12-20 https://zenodo.org/record/7464581 https://doi.org/10.5281/zenodo.7464581 eng eng doi:10.5281/zenodo.7112163 https://zenodo.org/record/7464581 https://doi.org/10.5281/zenodo.7464581 oai:zenodo.org:7464581 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/other dataset 2022 ftzenodo https://doi.org/10.5281/zenodo.746458110.5281/zenodo.7112163 2023-03-11T00:26:03Z 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. Changelog 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! Dataset Orca Zenodo |
institution |
Open Polar |
collection |
Zenodo |
op_collection_id |
ftzenodo |
language |
English |
description |
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. Changelog 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! |
format |
Dataset |
author |
Maldonado, Alex M. Poltavsky, Igor Vassilev-Galindo, Valentin Tkatchenko, Alexandre Keith, John A. |
spellingShingle |
Maldonado, Alex M. Poltavsky, Igor Vassilev-Galindo, Valentin Tkatchenko, Alexandre Keith, John A. Many-body machine learning models for water, acetonitrile, and methanol |
author_facet |
Maldonado, Alex M. Poltavsky, Igor Vassilev-Galindo, Valentin Tkatchenko, Alexandre Keith, John A. |
author_sort |
Maldonado, Alex M. |
title |
Many-body machine learning models for water, acetonitrile, and methanol |
title_short |
Many-body machine learning models for water, acetonitrile, and methanol |
title_full |
Many-body machine learning models for water, acetonitrile, and methanol |
title_fullStr |
Many-body machine learning models for water, acetonitrile, and methanol |
title_full_unstemmed |
Many-body machine learning models for water, acetonitrile, and methanol |
title_sort |
many-body machine learning models for water, acetonitrile, and methanol |
publishDate |
2022 |
url |
https://zenodo.org/record/7464581 https://doi.org/10.5281/zenodo.7464581 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
doi:10.5281/zenodo.7112163 https://zenodo.org/record/7464581 https://doi.org/10.5281/zenodo.7464581 oai:zenodo.org:7464581 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.746458110.5281/zenodo.7112163 |
_version_ |
1766161328996614144 |