Dataset

mlearn_Mo_test


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Name :
mlearn_Mo_test
Extended ID :
mlearn_Mo_test__Zuo-Chen-Li-Deng-Chen-Behler-Csanyi-Shapeev-Thompson-Wood-Ong__DS_l0b6iq3no012_0
ColabFit ID :
DS_l0b6iq3no012_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 Mo 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 :
10.60732/3db3283a https://commons.datacite.org/doi.org/10.60732/3db3283a https://doi.datacite.org/dois/10.60732%2F3db3283a https://doi.org/10.60732/3db3283a 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 Mo test." ColabFit, 2023. https://doi.org/10.60732/3db3283a.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
23
Num. Atoms :
1,189
Downloads :
20
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
Mo (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
Configuration Sets by Name :
Configuration Sets by ID :
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