Datasets:

colabfit
/

Transition1x-validation

configs:
  - config_name: default
    data_files: co/*.parquet
  - config_name: info
    data_files: ds.parquet
license: mit
tags:
  - molecular dynamics
  - mlip
  - interatomic potential
pretty_name: Transition1x-validation

Cite this dataset
Schreiner, M., Bhowmik, A., Vegge, T., Busk, J., and Winther, O. Transition1x-validation. ColabFit, 2023. https://doi.org/10.60732/8e8402d6

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_ktku4cml3al7_0

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Dataset Name

Transition1x-validation

Description

The validation split of the Transition1x dataset. Transition1x is a benchmark dataset containing 9.6 million Density Functional Theory (DFT) calculations of forces and energies of molecular configurations on and around reaction pathways at the ωB97x/6-31 G(d) level of theory. The configurations contained in this dataset allow a better representation of features in transition state regions when compared to other benchmark datasets -- in particular QM9 and ANI1x.

Dataset authors

Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, Ole Winther

Publication

https://doi.org/10.1038/s41597-022-01870-w

Original data link

https://doi.org/10.6084/m9.figshare.19614657.v4

License

MIT

Number of unique molecular configurations

264972

Number of atoms

3743153

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 Schreiner, M., Bhowmik, A., Vegge, T., Busk, J., and Winther, O. Transition1x-validation. ColabFit, 2023. https://doi.org/10.60732/8e8402d6