Dataset

Mo_PRM2019

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Name :

Mo_PRM2019

Extended ID :

Mo_PRM2019__Byggmastar-Nordlund-Djurabekova__DS_5aeg7va6k305_0

ColabFit ID :

DS_5aeg7va6k305_0

Files :

colabfitspec.json

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.

Num. Configurations :

3,785

Num. Atoms :

45,667

Downloads :

Calculated Property Types :

atomic_forces cauchy_stress energy

Elements :

Mo (100.0%)

Methods :

DFT-PBE

Software :

VASP

Publication Link :

https://doi.org/10.1103/PhysRevMaterials.4.093802

Data Source Link :

https://gitlab.com/acclab/gap-data/-/tree/master/Mo

Configuration Sets by Name :

Configuration Sets by ID :

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