Dataset

mlearn_Cu_train

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Name :

mlearn_Cu_train

Extended ID :

mlearn_Cu_train__Zuo-Chen-Li-Deng-Chen-Behler-Csanyi-Shapeev-Thompson-Wood-Ong__DS_3pv3hck35iy6_0

ColabFit ID :

DS_3pv3hck35iy6_0

Files :

colabfitspec.json

Description :

A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Cu configurations

Authors :

Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong

DOI :

10.60732/49de06ae https://commons.datacite.org/doi.org/10.60732/49de06ae https://doi.datacite.org/dois/10.60732%2F49de06ae https://doi.org/10.60732/49de06ae Cite as: Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. "mlearn Cu train." ColabFit, 2023. https://doi.org/10.60732/49de06ae.
For other citation formats, see the DataCite Fabrica page for this dataset.

Num. Configurations :

262

Num. Atoms :

27,416

Downloads :

Calculated Property Types :

atomic_forces cauchy_stress energy

Elements :

Cu (100.0%)

Methods :

DFT-PBE

Software :

VASP

Publication Link :

https://doi.org/10.1021/acs.jpca.9b08723

Data Source Link :

https://github.com/materialsvirtuallab/mlearn/tree/master/data

Configuration Sets by Name :

Configuration Sets by ID :

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