colabfit/W_LML-retrain_bulk_MD_test

W LML-retrain bulk MD test数据集是从W_LML-retrain数据集中提取的测试集，包含了对块状钨的计算。该数据集用于测试一个线性于描述符的机器学习势函数，这个势函数考虑了钨中位错-缺陷相互作用。密度泛函模拟使用了VASP软件，采用PBE泛函和投影增强波基组，截止能量为550 eV。占据数通过Methfessel-Paxton方法进行展宽，展宽宽度为0.1 eV。布里渊区采样使用了Monkhorst-Pack k点网格，对于沿位错线的二维簇模拟是周期的，而对于具有三维球形QM区域的计算使用了单个k点。这些参数的值是在对力进行一系列收敛测试后选择的，公差为几meV/Å。数据集中的额外细节存储在以"dataset_"为前缀的列中。数据集包含8个独特的分子配置和1996个原子，元素为W，包含的属性有能量、原子力和Cauchy应力。

The W LML-retrain bulk MD test dataset is a test set extracted from the W_LML-retrain dataset, containing calculations for bulk tungsten. This dataset is used to test a linear-in-descriptor machine learning potential that accounts for dislocation-defect interactions in tungsten. Density functional simulations were performed using VASP with the PBE functional and a projector augmented wave (PAW) basis set with a cutoff energy of 550 eV. Occupancies were smeared using 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 2D cluster simulations periodic along the dislocation line and a single k-point was used for 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. Additional details are stored in columns prefixed with dataset_. The dataset contains 8 unique molecular configurations and 1996 atoms, with the element W and properties including energy, atomic forces, and Cauchy stress.