Dataset

CA-9_training


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Name CA-9_training
Extended ID CA-9_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_9cdtww9pcjqd_0
Description Configurations from CA-9 dataset used for training NNP_CA-9 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
Authors Daniel Hedman
Tom Rothe
Gustav Johansson
Fredrik Sandin
J. Andreas Larsson
Yoshiyuki Miyamoto
DOI 10.60732/8b765383
https://commons.datacite.org/doi.org/10.60732/8b765383
https://doi.datacite.org/dois/10.60732%2F8b765383
https://doi.org/10.60732/8b765383

Cite as: Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. "CA-9 training." ColabFit, 2023. https://doi.org/10.60732/8b765383.
For other citation formats, see the DataCite Fabrica page for this dataset.
Calculated Property Types atomic_forces
cauchy_stress
energy
Elements C (100.0%)
Number of Configurations 40,000
Number of Atoms 2,195,399
Links https://doi.org/10.24435/materialscloud:6h-yj
https://doi.org/10.1016/j.cartre.2021.100027
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Calculated Properties
ColabFit ID DS_9cdtww9pcjqd_0
Files colabfitspec.json

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