solvated_protein_fragments_JCTC_2019 ...
The solvated protein fragments dataset was generated as a partner benchmark dataset, along with SN2, for measuring the performance of machine learning models, in particular PhysNet, at describing chemical reactions, long-range interactions, and condensed phase systems. The dataset contains structure...
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2023
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Online Access: | https://dx.doi.org/10.60732/c4731f07 https://materials.colabfit.org/id/DS_ctjgc03xdauc_0 |
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ftdatacite:10.60732/c4731f07 2024-04-28T08:35:17+00:00 solvated_protein_fragments_JCTC_2019 ... Unke, Oliver T. Meuwly, Markus 2023 chemical/x-xyz https://dx.doi.org/10.60732/c4731f07 https://materials.colabfit.org/id/DS_ctjgc03xdauc_0 en eng ColabFit https://doi.org/10.1021/acs.jctc.9b00181 https://doi.org/10.5281/zenodo.2605372 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/c4731f07 2024-04-02T12:53:40Z The solvated protein fragments dataset was generated as a partner benchmark dataset, along with SN2, for measuring the performance of machine learning models, in particular PhysNet, at describing chemical reactions, long-range interactions, and condensed phase systems. The dataset contains structures for all possible "amons" (hydrogen-saturated covalently bonded fragments) of up to eight heavy atoms (C, N, O, S) that can be derived from chemical graphs of proteins containing the 20 natural amino acids connected via peptide bonds or disulfide bridges. For amino acids that can occur in different charge states due to (de)protonation (i.e., carboxylic acids that can be negatively charged or amines that can be positively charged), all possible structures with up to a total charge of +-2e are included. In total, the dataset provides reference energies, forces, and dipole moments for 2,731,180 structures calculated at the revPBE-D3(BJ)/def2-TZVP level of theory using ORCA 4.0.1. ... Dataset Orca DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
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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 Unke, Oliver T. Meuwly, Markus solvated_protein_fragments_JCTC_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 |
The solvated protein fragments dataset was generated as a partner benchmark dataset, along with SN2, for measuring the performance of machine learning models, in particular PhysNet, at describing chemical reactions, long-range interactions, and condensed phase systems. The dataset contains structures for all possible "amons" (hydrogen-saturated covalently bonded fragments) of up to eight heavy atoms (C, N, O, S) that can be derived from chemical graphs of proteins containing the 20 natural amino acids connected via peptide bonds or disulfide bridges. For amino acids that can occur in different charge states due to (de)protonation (i.e., carboxylic acids that can be negatively charged or amines that can be positively charged), all possible structures with up to a total charge of +-2e are included. In total, the dataset provides reference energies, forces, and dipole moments for 2,731,180 structures calculated at the revPBE-D3(BJ)/def2-TZVP level of theory using ORCA 4.0.1. ... |
format |
Dataset |
author |
Unke, Oliver T. Meuwly, Markus |
author_facet |
Unke, Oliver T. Meuwly, Markus |
author_sort |
Unke, Oliver T. |
title |
solvated_protein_fragments_JCTC_2019 ... |
title_short |
solvated_protein_fragments_JCTC_2019 ... |
title_full |
solvated_protein_fragments_JCTC_2019 ... |
title_fullStr |
solvated_protein_fragments_JCTC_2019 ... |
title_full_unstemmed |
solvated_protein_fragments_JCTC_2019 ... |
title_sort |
solvated_protein_fragments_jctc_2019 ... |
publisher |
ColabFit |
publishDate |
2023 |
url |
https://dx.doi.org/10.60732/c4731f07 https://materials.colabfit.org/id/DS_ctjgc03xdauc_0 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
https://doi.org/10.1021/acs.jctc.9b00181 https://doi.org/10.5281/zenodo.2605372 |
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/c4731f07 |
_version_ |
1797567426426044416 |