Datasets:

colabfit
/

WS22_sma

configs:
  - config_name: default
    data_files: co/*.parquet
  - config_name: info
    data_files: ds.parquet
license: cc-by-4.0
tags:
  - molecular dynamics
  - mlip
  - interatomic potential
pretty_name: WS22 sma

Cite this dataset
Jr, M. P., Zhang, S., Dral, P. O., and Barbatti, M. WS22 sma. ColabFit, 2023. https://doi.org/10.60732/bd5d6dc9

This dataset has been curated and formatted for the ColabFit Exchange

This dataset is also available on the ColabFit Exchange:

https://materials.colabfit.org/id/DS_jz8yny1m7xer_0

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Dataset Name

WS22 sma

Description

Configurations of sma from WS22. The WS22 database combines Wigner sampling with geometry interpolation to generate 1.18 million molecular geometries equally distributed into 10 independent datasets of flexible organic molecules with varying sizes and chemical complexity. In addition to the potential energy and forces required to construct potential energy surfaces, the WS22 database provides several other quantum chemical properties, all obtained via single-point calculations for each molecular geometry. All quantum chemical calculations were performed with the Gaussian 09 program.

Dataset authors

Max Pinheiro Jr, Shuang Zhang, Pavlo O. Dral, Mario Barbatti

Publication

https://doi.org/10.1038/s41597-023-01998-3

Original data link

https://doi.org/10.5281/zenodo.7032333

License

CC-BY-4.0

Number of unique molecular configurations

120028

Number of atoms

2280532

Elements included

C, H, N, O

Properties included

energy, atomic forces

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).

Cite this dataset Jr, M. P., Zhang, S., Dral, P. O., and Barbatti, M. WS22 sma. ColabFit, 2023. https://doi.org/10.60732/bd5d6dc9