Datasets:

colabfit
/

ABC2D6-16_PRL_2018

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-3.0
tags:
  - molecular dynamics
  - mlip
  - interatomic potential
pretty_name: ABC2D6-16 PRL 2018

Cite this dataset
Faber, F., Lindmaa, A., Lilienfeld, O. A., and Armiento, R. ABC2D6-16 PRL 2018. ColabFit, 2023. https://doi.org/10.60732/f87a64e4

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_0ady7a8a8n6p_0

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Dataset Name

ABC2D6-16 PRL 2018

Description

Dataset used to train a machine learning model to calculate density functional theory-quality formation energies of all ~2 x 106 pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth).

Dataset authors

Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld, Rickard Armiento

Publication

https://doi.org/10.1103/PhysRevLett.117.135502

Original data link

https://qmml.org/datasets.html

License

CC-BY-3.0

Number of unique molecular configurations

21881

Number of atoms

218810

Elements included

Al, Ar, As, B, Ba, Be, Bi, Br, C, Ca, Cl, Cs, F, Ga, Ge, H, He, I, In, K, Kr, Li, Mg, N, Na, Ne, O, P, Pb, Rb, S, Sb, Se, Si, Sn, Sr, Te, Tl, Xe

Properties included

formation energy

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 Faber, F., Lindmaa, A., Lilienfeld, O. A., and Armiento, R. ABC2D6-16 PRL 2018. ColabFit, 2023. https://doi.org/10.60732/f87a64e4