Dataset

Mo_PRM2019



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Name Mo_PRM2019
Extended ID Mo_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_5aeg7va6k305_0
Description This dataset was designed to enable machine learning of Mo 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 10.60732/31dbb6ee
https://commons.datacite.org/doi.org/10.60732/31dbb6ee
https://doi.datacite.org/dois/10.60732%2F31dbb6ee
https://doi.org/10.60732/31dbb6ee

Cite as: Byggmästar, J., Nordlund, K., and Djurabekova, F. "Mo PRM2019." ColabFit, 2023. https://doi.org/10.60732/31dbb6ee.
For other citation formats, see the DataCite Fabrica page for this dataset.
Calculated Property Types atomic_forces
cauchy_stress
energy
Elements Mo (100.0%)
Number of Configurations 3,785
Number of Atoms 45,667
Links https://gitlab.com/acclab/gap-data/-/tree/master/Mo
https://doi.org/10.1103/PhysRevMaterials.4.093802
Configuration Sets by Name
Configuration Sets by ID
Calculated Properties
ColabFit ID DS_5aeg7va6k305_0
Files colabfitspec.json

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