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Name W_LML-retrain_bulk_MD_test
Extended ID W_LML-retrain_bulk_MD_test_OnatOrtnerKermode__DS_0od3fns1ap8a_0
Description Test set from W_LML-retrain dataset, containing bulk tungsten calculations. The W_LML-retrain dataset contains DFT calculations used in testing a linear-in-descriptor machine learning potential that accounts for dislocation-defect interactions in tungsten. Density functional simulations were performed using VASP. The PBE generalised gradient approximation was used to describe effects of electron exchange and correlation together with a projector augmented wave (PAW) basis set with a cut-off energy of 550 eV. Occupancies were smeared with a Methfessel-Paxton scheme of order one with a 0.1 eV smearing width. The Brillouin zone was sampled with a Monkhorst-Pack k-point grid for the 2D cluster simulations periodic along the dislocation line and a single k-point was used for the calculations with 3D spherical QM regions. The values of these parameters were chosen after a series of convergence tests on forces with a tolerance of a few meV/Å.
Authors Berk Onat
Christoph Ortner
James R. Kermode
DOI 10.60732/9d48595f

Cite as: Onat, B., Ortner, C., and Kermode, J. R. "W LML-retrain bulk MD test." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements W (100.0%)
Number of Data Objects 8
Number of Configurations 8
Number of Atoms 1,996
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_0od3fns1ap8a_0
Files colabfitspec.json

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