Datasets:

colabfit
/

JARVIS_EPC_2D

configs:
  - config_name: default
    data_files: co/*.parquet
  - config_name: info
    data_files: ds.parquet
license: cc-by-4.0
tags:
  - molecular dynamics
  - mlip
  - interatomic potential
pretty_name: JARVIS EPC 2D

Cite this dataset
Wines, D., Choudhary, K., Biacchi, A. J., Garrity, F., and Tavazza, F. JARVIS EPC 2D. ColabFit, 2023. https://doi.org/10.60732/c7d2c9cd

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_7jji22dy5hix_0

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Dataset Name

JARVIS EPC 2D

Description

The JARVIS_EPC_2D dataset is part of the joint automated repository for various integrated simulations (JARVIS) DFT database. This subset contains configurations sourced from the JARVIS-DFT-2D dataset, rerelaxed with Quantum ESPRESSO. JARVIS is a set of tools and datasets built to meet current materials design challenges.

Dataset authors

Daniel Wines, Kamal Choudhary, Adam J. Biacchi, evin F. Garrity, Francesca Tavazza

Publication

https://doi.org/10.1021/acs.nanolett.2c04420

Original data link

https://figshare.com/ndownloader/files/38950433

License

CC-BY-4.0

Number of unique molecular configurations

161

Number of atoms

788

Elements included

Ag, Al, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cl, Co, Cr, Cu, F, Fe, Ga, Ge, H, Hf, I, In, Ir, K, La, Li, Mg, Mo, N, Na, Nb, Ni, O, P, Pb, Pd, Pt, Ru, S, Sb, Sc, Se, Si, Sn, Sr, Ta, Te, Ti, Tl, V, W, Y, Zn, Zr

Properties included

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 Wines, D., Choudhary, K., Biacchi, A. J., Garrity, F., and Tavazza, F. JARVIS EPC 2D. ColabFit, 2023. https://doi.org/10.60732/c7d2c9cd