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_i70ao22y4mha_0
CS_r3g1taa0bwsq_0
CS_hi3ggp6olsqy_0
CS_j7xkemgigmt6_0
CS_8jsich5xzdkk_0
CS_xnpld8xmod2z_0
CS_b68ukf9mvdxc_0
CS_j68aio8caac6_0
CS_94xb3bb9nrdr_0
CS_jvvrmie6xcjy_0
Calculated Properties
ColabFit ID DS_xzaglubh0trq_0
Files colabfitspec.json

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