W_LML-retrain_bulk_MD_test

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.