id ftdatacite:10.60732/b0a10262
record_format openpolar
spelling ftdatacite:10.60732/b0a10262 2024-04-28T08:35:14+00:00 QM_hamiltonian_nature_2019 ... Schütt, Kristof T. Gastegger, Michael Tkatchenko, Alexandre Klaus-Robert Müller Maurer, Reinhard J. 2023 chemical/x-xyz https://dx.doi.org/10.60732/b0a10262 https://materials.colabfit.org/id/DS_02cqe6a0bobu_0 en eng ColabFit https://doi.org/10.1038/s41467-019-12875-2 http://quantum-machine.org/datasets/ Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 ColabFit Dataset AgPd_NPJ_2021 Materials Science https//id.loc.gov/authorities/subjects/sh85082094.html Machine Learning https//id.loc.gov/authorities/subjects/sh85079324.html Techniques/Computational Techniques/First-principles calculations Techniques/Computational Techniques/Machine Learning Techniques/Computational Techniques/Molecular Dynamics Research Areas/Atomic & molecular structure/Potential energy surfaces Research Areas/Electronic structure/Interatomic & molecular potentials dataset ColabFit Dataset Dataset 2023 ftdatacite https://doi.org/10.60732/b0a10262 2024-04-02T12:53:40Z ~100,000 configurations of water, ethanol, malondialdehyde and uracil gathered at the PBE/def2-SVP level of theory using ORCA. ... Dataset Orca DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic ColabFit
Dataset
AgPd_NPJ_2021
Materials Science
https//id.loc.gov/authorities/subjects/sh85082094.html
Machine Learning
https//id.loc.gov/authorities/subjects/sh85079324.html
Techniques/Computational Techniques/First-principles calculations
Techniques/Computational Techniques/Machine Learning
Techniques/Computational Techniques/Molecular Dynamics
Research Areas/Atomic & molecular structure/Potential energy surfaces
Research Areas/Electronic structure/Interatomic & molecular potentials
spellingShingle ColabFit
Dataset
AgPd_NPJ_2021
Materials Science
https//id.loc.gov/authorities/subjects/sh85082094.html
Machine Learning
https//id.loc.gov/authorities/subjects/sh85079324.html
Techniques/Computational Techniques/First-principles calculations
Techniques/Computational Techniques/Machine Learning
Techniques/Computational Techniques/Molecular Dynamics
Research Areas/Atomic & molecular structure/Potential energy surfaces
Research Areas/Electronic structure/Interatomic & molecular potentials
Schütt, Kristof T.
Gastegger, Michael
Tkatchenko, Alexandre
Klaus-Robert Müller
Maurer, Reinhard J.
QM_hamiltonian_nature_2019 ...
topic_facet ColabFit
Dataset
AgPd_NPJ_2021
Materials Science
https//id.loc.gov/authorities/subjects/sh85082094.html
Machine Learning
https//id.loc.gov/authorities/subjects/sh85079324.html
Techniques/Computational Techniques/First-principles calculations
Techniques/Computational Techniques/Machine Learning
Techniques/Computational Techniques/Molecular Dynamics
Research Areas/Atomic & molecular structure/Potential energy surfaces
Research Areas/Electronic structure/Interatomic & molecular potentials
description ~100,000 configurations of water, ethanol, malondialdehyde and uracil gathered at the PBE/def2-SVP level of theory using ORCA. ...
format Dataset
author Schütt, Kristof T.
Gastegger, Michael
Tkatchenko, Alexandre
Klaus-Robert Müller
Maurer, Reinhard J.
author_facet Schütt, Kristof T.
Gastegger, Michael
Tkatchenko, Alexandre
Klaus-Robert Müller
Maurer, Reinhard J.
author_sort Schütt, Kristof T.
title QM_hamiltonian_nature_2019 ...
title_short QM_hamiltonian_nature_2019 ...
title_full QM_hamiltonian_nature_2019 ...
title_fullStr QM_hamiltonian_nature_2019 ...
title_full_unstemmed QM_hamiltonian_nature_2019 ...
title_sort qm_hamiltonian_nature_2019 ...
publisher ColabFit
publishDate 2023
url https://dx.doi.org/10.60732/b0a10262
https://materials.colabfit.org/id/DS_02cqe6a0bobu_0
genre Orca
genre_facet Orca
op_relation https://doi.org/10.1038/s41467-019-12875-2
http://quantum-machine.org/datasets/
op_rights Creative Commons Zero v1.0 Universal
https://creativecommons.org/publicdomain/zero/1.0/legalcode
cc0-1.0
op_doi https://doi.org/10.60732/b0a10262
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