Dataset

cG-SchNet


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Species content of dataset


Name :
cG-SchNet
Authors :
Niklas W.A. Gebauer, Michael Gastegger, Stefaan S.P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt
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.
DOI :
https://doi.org/10.60732/de8af6a2 https://commons.datacite.org/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.
ColabFit ID :
DS_xzaglubh0trq_0
Extended ID :
cG-SchNet__Gebauer-Gastegger-Hessmann-Muller-Schutt__DS_xzaglubh0trq_0
Date Added :
2023-06-30
License :
MIT
Downloads :
47
Num. Configurations :
23,632
Num. Atoms :
418,729
Calculated Property Types :
energy
Elements :
C (38.45%) F (0.05%) H (49.14%) N (3.21%) O (9.15%)
Methods :
IP-cgSchNet
Software :
ORCA
Publication Link :
https://doi.org/10.1038/s41467-022-28526-y
Data Source Link :
https://github.com/atomistic-machine-learning/cG-SchNet/
Spec File :
colabfitspec.json
Configuration Sets by Name :
Configuration Sets by ID :
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