Datasets:

colabfit
/

Mo_PRM2019

configs:
  - config_name: default
    data_files: co/*.parquet
  - config_name: info
    data_files: ds.parquet
  - config_name: configuration_sets
    data_files: cs/*.parquet
  - config_name: config_set_mapping
    data_files: cs_co_map/*.parquet
license: cc-by-4.0
tags:
  - molecular dynamics
  - mlip
  - interatomic potential
pretty_name: Mo PRM2019

Cite this dataset
Byggmästar, J., Nordlund, K., and Djurabekova, F. Mo PRM2019. ColabFit, 2023. https://doi.org/10.60732/31dbb6ee

This dataset has been curated and formatted for the ColabFit Exchange

This dataset is also available on the ColabFit Exchange:

https://materials.colabfit.org/id/DS_5aeg7va6k305_0

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https://materials.colabfit.org

Dataset Name

Mo PRM2019

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.

Dataset authors

Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova

Publication

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

Original data link

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

License

CC-BY-4.0

Number of unique molecular configurations

3785

Number of atoms

45667

Elements included

Properties included

energy, atomic forces, cauchy stress

Usage

ds.parquet : Aggregated dataset information.
co/ directory: Configuration rows each include a structure, calculated properties, and metadata.
cs/ directory : Configuration sets are subsets of configurations grouped by some common characteristic. If cs/ does not exist, no configurations sets have been defined for this dataset.
cs_co_map/ directory : The mapping of configurations to configuration sets (if defined).

Cite this dataset Byggmästar, J., Nordlund, K., and Djurabekova, F. Mo PRM2019. ColabFit, 2023. https://doi.org/10.60732/31dbb6ee