cG-SchNet ...

Configurations from a cG-SchNet trained on a subset of the QM9dataset. Model was trained with the intention of providing molecules withspecified functional groups or motifs, relying on sampling of molecularfingerprint data. Relaxation data for the generated molecules is computedusing ORCA software....

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Bibliographic Details
Main Authors: Gebauer, Niklas W.A., Gastegger, Michael, Hessmann, Stefaan S.P., Klaus-Robert Müller, Schütt, Kristof T.
Format: Dataset
Language:English
Published: ColabFit 2023
Subjects:
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
Orca
Online Access:https://dx.doi.org/10.60732/de8af6a2
https://materials.colabfit.org/id/DS_xzaglubh0trq_0
Description
Summary:Configurations from a cG-SchNet trained on a subset of the QM9dataset. Model was trained with the intention of providing molecules withspecified functional groups or motifs, relying on sampling of molecularfingerprint data. Relaxation data for the generated molecules is computedusing ORCA software. Configuration sets include raw data fromcG-SchNet-generated configurations, with models trained on several differenttypes of target data and DFT relaxation data as a separate configurationset. Includes approximately 80,000 configurations. ...

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