Dataset

CA-9_test




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Name CA-9_test
Extended ID CA-9_test__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_h0mshvvbxlai_0
Description Test configurations from CA-9 dataset used to evaluate trained NNPs.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 10.60732/5a57f6ad
https://commons.datacite.org/doi.org/10.60732/5a57f6ad
https://doi.datacite.org/dois/10.60732%2F5a57f6ad
https://doi.org/10.60732/5a57f6ad

Cite as: Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. "CA-9 test." ColabFit, 2023. https://doi.org/10.60732/5a57f6ad.
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 2,726
Number of Atoms 206,238
Publication Link https://doi.org/10.1016/j.cartre.2021.100027
Data Source Link https://doi.org/10.24435/materialscloud:6h-yj
Configuration Sets by Name
Configuration Sets by ID
ColabFit ID DS_h0mshvvbxlai_0
Downloads 10
Files colabfitspec.json

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