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----
-configs:
-- config_name: default
-  data_files: "main/*.parquet"
-license: mit
-tags:
-- molecular dynamics
-- mlip
-- interatomic potential
-pretty_name: cG-SchNet
----
-# Dataset  
-cG-SchNet  
-### 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.  
-<br>Additional details stored in dataset columns prepended with "dataset_".  
-### Dataset authors  
-Niklas W.A. Gebauer, Michael Gastegger, Stefaan S.P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt  
-### Publication  
-https://doi.org/10.1038/s41467-022-28526-y  
-### Original data link 
-https://github.com/atomistic-machine-learning/cG-SchNet/  
-### License  
-MIT  
-### Number of unique molecular configurations  
-79772  
-### Number of atoms  
-1467492  
-### Elements included  
-C, H, N, O, F  
-### Properties included  
-energy, atomic forces, cauchy stress  
-### Cite this dataset
-Gebauer, N. W., Gastegger, M., Hessmann, S. S., Müller, K., and Schütt, K. T. _cG-SchNet_. ColabFit, 2023. https://doi.org/10.60732/de8af6a2