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 Calculated Property Types: cauchy_stress energy Elements: W (100.0%) Methods: DFT-PBE Software: VASP Number of Configurations: 8 Number of Atoms: 1,996 Publication Link: https://doi.org/10.1016/j.actamat.2023.118734 Data Source Link: https://github.com/marseille-matmol/LML-retrain