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.

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

23632

Number of atoms

418729

Elements included

C, F, H, N, O

Properties included

energy

Usage

ds.parquet : Aggregated dataset information.
co/ directory: Configuration rows each include a structure, calculated properties, and metadata.
cs/ directory : Configuration sets are subsets of configurations grouped by some common characteristic. If cs/ does not exist, no configurations sets have been defined for this dataset.
cs_co_map/ directory : The mapping of configurations to configuration sets (if defined).

ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:

Downloads last month: 18

Total file size:

13.7 MB

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

This dataset has been curated and formatted for the ColabFit Exchange

This dataset is also available on the ColabFit Exchange:

Visit the ColabFit Exchange to search additional datasets by author, description, element content and more.

Dataset Name

Description