Dataset

CA-9_BR_training

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Name :

CA-9_BR_training

Extended ID :

CA-9_BR_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_fw7m5d8b0fa9_0

ColabFit ID :

DS_fw7m5d8b0fa9_0

Files :

colabfitspec.json

Description :

Binning-random configurations from CA-9 dataset used for training NNP_BR 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 :

10.60732/07b7d297 https://commons.datacite.org/doi.org/10.60732/07b7d297 https://doi.datacite.org/dois/10.60732%2F07b7d297 https://doi.org/10.60732/07b7d297 Cite as: Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. "CA-9 BR training." ColabFit, 2023. https://doi.org/10.60732/07b7d297.
For other citation formats, see the DataCite Fabrica page for this dataset.

Num. Configurations :

20,013

Num. Atoms :

1,072,779

Downloads :

Calculated Property Types :

atomic_forces cauchy_stress energy

Elements :

C (100.0%)

Methods :

DFT-PBE

Software :

VASP

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 :

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