Datasets:

colabfit
/

DFT_polymorphs_PNAS_2022_PBE0_MBD_benzene_train

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: DFT polymorphs PNAS 2022 PBE0 MBD benzene train

Cite this dataset
Kapil, V., and Engel, E. A. DFT polymorphs PNAS 2022 PBE0 MBD benzene train. ColabFit, 2023. https://doi.org/10.60732/8d563e8a

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_ce4zytu7ph7e_0

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Dataset Name

DFT polymorphs PNAS 2022 PBE0 MBD benzene train

Description

Benzene training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction.

Dataset authors

Venkat Kapil, Edgar A. Engel

Publication

https://doi.org/10.1073/pnas.2111769119

Original data link

https://doi.org/10.24435/materialscloud:vp-jf

License

CC-BY-4.0

Number of unique molecular configurations

1799

Number of atoms

49512

Elements included

C, H

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 Kapil, V., and Engel, E. A. DFT polymorphs PNAS 2022 PBE0 MBD benzene train. ColabFit, 2023. https://doi.org/10.60732/8d563e8a