Dataset
W_LML-retrain_bulk_MD_test
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| Name | W_LML-retrain_bulk_MD_test |
|---|---|
| Extended ID | W_LML-retrain_bulk_MD_test__Onat-Ortner-Kermode__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
https://commons.datacite.org/doi.org/10.60732/9d48595f https://doi.datacite.org/dois/10.60732%2F9d48595f https://doi.org/10.60732/9d48595f Cite as: Onat, B., Ortner, C., and Kermode, J. R. "W LML-retrain bulk MD test." ColabFit, 2023. https://doi.org/10.60732/9d48595f. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
W (100.0%) |
| Number of Configurations | 8 |
| Number of Atoms | 1,996 |
| Links |
https://github.com/marseille-matmol/LML-retrain https://doi.org/10.1016/j.actamat.2023.118734 |
| Configuration Sets by Name | (None) |
| Configuration Sets by ID | (None) |
| Calculated Properties | |
| ColabFit ID | DS_0od3fns1ap8a_0 |
| Files | colabfitspec.json |
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