Dataset D130

Export metadata Download data Report issue

Attribution

Contact details

Alexandra GORYAEVA, Saclay Nuclear Research Centre (CEA Saclay)

Email: alexandra.goryaeva@cea.fr

Additional attribution details

email: alexandra.goryaeva@cea.fr
alternative contact: mihai-cosmin.marinica@cea.fr

Acknowledgements

Cross-Disciplinary Program on Numerical Simulation of CEA, the French Alternative Energies and Atomic Energy Commission.  
GENCI - (CINES/CCRT) computer centre under Grant No. A0070906973.

Citation

B13: A. M. Goryaeva, J. Maillet, M. Marinica, "Towards better efficiency of interatomic linear machine learning potentials", Computational Materials Science 166, 200-209 (2019). [link to article]

Content

System composition: Fe_129

Number of atoms: 129

Number of atom types: 1

Matrix: Fe

Structure: bcc

Reference (bulk) structure calculation: D77

Point Defects

self-interstitial: 1 Fe

Content comments:

different mono self-interstitials in bcc Fe with a0=2.8553  : 100, 110, 111, octa and tetra

Calculation

Calculation type: structural relaxation

Code: VASP 5.4.1

Exchange correlation: GGA

Exchange correlation comment: GGA

kpoints: 5 5 5 0.5 0.5 0.5

Ecut: 400.0 eV

Smearing type: Methfessel-Paxton

Smearing energy: 0.25 eV

Electronic density convergence criterion: 0.0000001

Magnetism included? Yes

Calculation comments:

The uploaded defect configurations are accompanied by INCAR, OUTCAR, KPOINTS, CONTCAR and POSCAR

Pseudopotential

Name: "PAW_PBE Fe_pv"

Class: paw

Semicore? Yes

DataSet