Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials
Input, output, and trajectories of molecular dynamics (MD) simulations of water, acetonitrile, and methanol. Simulations were driven by many-body machine learning (mbML) potentials including explicit 1-, 2-, and 3-body contributions. GDML, GAP, and SchNet models are provided in a separate repository...
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ftzenodo:oai:zenodo.org:7112198 2023-05-15T17:53:47+02:00 Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials Maldonado, Alex M. 2022-09-26 https://zenodo.org/record/7112198 https://doi.org/10.5281/zenodo.7112198 eng eng doi:10.5281/zenodo.7112163 doi:10.5281/zenodo.6270373 doi:10.5281/zenodo.6508586 doi:10.5281/zenodo.7112197 https://zenodo.org/record/7112198 https://doi.org/10.5281/zenodo.7112198 oai:zenodo.org:7112198 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.711219810.5281/zenodo.711216310.5281/zenodo.627037310.5281/zenodo.650858610.5281/zenodo.7112197 2023-03-11T02:33:37Z Input, output, and trajectories of molecular dynamics (MD) simulations of water, acetonitrile, and methanol. Simulations were driven by many-body machine learning (mbML) potentials including explicit 1-, 2-, and 3-body contributions. GDML, GAP, and SchNet models are provided in a separate repository. All simulations were performed in the atomic simulation environment (ASE). Analyses including radial distribution function (rdf) curves are provided here. Manifest The following simulations are included in this repository for each solvent. 1 ps hexamer NVE MD simulation driven by MP2/def2-TZVP (in ORCA v4.2.0), mbGDML, mbGAP, mbSchNet, and GFN2-xTB started with the same positions and velocities. Velocities were initialized at 298.15 K with a Maxwell-Boltzmann distribution. Periodic NVT MD simulation at 298.15 K for 10 or 30 ps with a 1 fs time step driven by mbGDML. These simulations contained 58 or 137 water molecules, 67 or 122 acetonitrile molecules, 61 methanol molecules. Systems that are not bolded were used for testing purposes. Dataset Orca Zenodo |
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Input, output, and trajectories of molecular dynamics (MD) simulations of water, acetonitrile, and methanol. Simulations were driven by many-body machine learning (mbML) potentials including explicit 1-, 2-, and 3-body contributions. GDML, GAP, and SchNet models are provided in a separate repository. All simulations were performed in the atomic simulation environment (ASE). Analyses including radial distribution function (rdf) curves are provided here. Manifest The following simulations are included in this repository for each solvent. 1 ps hexamer NVE MD simulation driven by MP2/def2-TZVP (in ORCA v4.2.0), mbGDML, mbGAP, mbSchNet, and GFN2-xTB started with the same positions and velocities. Velocities were initialized at 298.15 K with a Maxwell-Boltzmann distribution. Periodic NVT MD simulation at 298.15 K for 10 or 30 ps with a 1 fs time step driven by mbGDML. These simulations contained 58 or 137 water molecules, 67 or 122 acetonitrile molecules, 61 methanol molecules. Systems that are not bolded were used for testing purposes. |
format |
Dataset |
author |
Maldonado, Alex M. |
spellingShingle |
Maldonado, Alex M. Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials |
author_facet |
Maldonado, Alex M. |
author_sort |
Maldonado, Alex M. |
title |
Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials |
title_short |
Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials |
title_full |
Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials |
title_fullStr |
Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials |
title_full_unstemmed |
Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials |
title_sort |
water, acetonitrile, and methanol md simulations driven by many-body ml potentials |
publishDate |
2022 |
url |
https://zenodo.org/record/7112198 https://doi.org/10.5281/zenodo.7112198 |
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Orca |
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Orca |
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doi:10.5281/zenodo.7112163 doi:10.5281/zenodo.6270373 doi:10.5281/zenodo.6508586 doi:10.5281/zenodo.7112197 https://zenodo.org/record/7112198 https://doi.org/10.5281/zenodo.7112198 oai:zenodo.org:7112198 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.711219810.5281/zenodo.711216310.5281/zenodo.627037310.5281/zenodo.650858610.5281/zenodo.7112197 |
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1766161485226049536 |