Datasets:

colabfit
/

CA-9_BB_training

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: CA-9 BB training

Cite this dataset
Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. CA-9 BB training. ColabFit, 2023. https://doi.org/10.60732/f3bbbd36

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_l7inbtql4ea9_0

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https://materials.colabfit.org

Dataset Name

CA-9 BB training

Description

Binning-binning configurations from CA-9 dataset used for training NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

Dataset authors

Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto

Publication

https://doi.org/10.1016/j.cartre.2021.100027

Original data link

https://doi.org/10.24435/materialscloud:6h-yj

License

CC-BY-4.0

Number of unique molecular configurations

20006

Number of atoms

1053753

Elements included

Properties included

energy, atomic forces, cauchy stress

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 Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. CA-9 BB training. ColabFit, 2023. https://doi.org/10.60732/f3bbbd36