cG-SchNet ...

• 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

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. ...