Dataset
Mo_PRM2019
Species content of dataset
Dataset viewer powered by Hugging Face
| Name | Mo_PRM2019 |
|---|---|
| Extended ID | Mo_PRM2019__Byggmastar-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 |
| 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 | |
| ColabFit ID | DS_5aeg7va6k305_0 |
| Downloads | 10 |
| Files | colabfitspec.json |
No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.