Dataset

Ta_PRM2019



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Name Ta_PRM2019
Extended ID Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Description This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
Authors Jesper Byggmästar
Kai Nordlund
Flyura Djurabekova
DOI 10.60732/43837a12
https://commons.datacite.org/doi.org/10.60732/43837a12
https://doi.datacite.org/dois/10.60732%2F43837a12
https://doi.org/10.60732/43837a12

Cite as: Byggmästar, J., Nordlund, K., and Djurabekova, F. "Ta PRM2019." ColabFit, 2023. https://doi.org/10.60732/43837a12.
For other citation formats, see the DataCite Fabrica page for this dataset.
Calculated Property Types atomic_forces
cauchy_stress
energy
Elements Ta (100.0%)
Number of Configurations 3,775
Number of Atoms 45,439
Links https://gitlab.com/acclab/gap-data/-/tree/master
https://doi.org/10.1103/PhysRevMaterials.4.093802
Configuration Sets by Name
Configuration Sets by ID
Calculated Properties
ColabFit ID DS_40zw467dnc6d_0
Files colabfitspec.json

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