Dataset
cG-SchNet
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| Name | cG-SchNet |
|---|---|
| Extended ID | cG-SchNet__Gebauer-Gastegger-Hessmann-Müller-Schütt__DS_xzaglubh0trq_0 |
| Description | 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. |
| Authors |
Niklas W.A. Gebauer Michael Gastegger Stefaan S.P. Hessmann Klaus-Robert Müller Kristof T. Schütt |
| DOI |
10.60732/de8af6a2
https://commons.datacite.org/doi.org/10.60732/de8af6a2 https://doi.datacite.org/dois/10.60732%2Fde8af6a2 https://doi.org/10.60732/de8af6a2 Cite as: Gebauer, N. W., Gastegger, M., Hessmann, S. S., Müller, K., and Schütt, K. T. "cG-SchNet." ColabFit, 2023. https://doi.org/10.60732/de8af6a2. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
C (35.93%) F (0.16%) H (51.0%) N (4.96%) O (7.96%) |
| Number of Configurations | 79,772 |
| Number of Atoms | 1,467,492 |
| Links |
https://github.com/atomistic-machine-learning/cG-SchNet/ https://doi.org/10.1038/s41467-022-28526-y |
| Configuration Sets by Name |
cG-SchNet_polarizability_predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on isotropic polarizability data cG-SchNet_polarizability_computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on isotropic polarizability data cG-SchNet_fingerprint_predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on vector-valued molecular fingerprints cG-SchNet_fingerprint_computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on vector-valued molecular fingerprints cG-SchNet_gap_predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on HOMO-LUMO gap data cG-SchNet_gap_computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on HOMO-LUMO gap data cG-SchNet_composition_relative_energy_predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on atomic composition and relative atomic energy data cG-SchNet_composition_relative_energy_computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on atomic composition and relative atomic energy data cG-SchNet_gap_relative_energy_predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on HOMO-LUMO gap and relative atomic energy data cG-SchNet_gap_computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on HOMO-LUMO gap and relative atomic energy data |
| Configuration Sets by ID |
CS_8jsich5xzdkk_0 CS_94xb3bb9nrdr_0 CS_b68ukf9mvdxc_0 CS_hi3ggp6olsqy_0 CS_i70ao22y4mha_0 CS_j68aio8caac6_0 CS_j7xkemgigmt6_0 CS_jvvrmie6xcjy_0 CS_r3g1taa0bwsq_0 CS_xnpld8xmod2z_0 |
| Calculated Properties | |
| ColabFit ID | DS_xzaglubh0trq_0 |
| Files | colabfitspec.json |
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