colabfit/Silica_NPJCM_2022

Silica NPJCM 2022数据集是为了训练硅（SiO2）的机器学习原子间势能（MLIP）而创建的。初始的DFT计算使用了GPAW（与ASE结合）以及LDA、PBE和PBEsol功能；还使用了VASP和SCAN功能。所有计算均采用了投影增强波方法。经过比较，发现SCAN表现最佳，因此所有值都使用SCAN重新计算。使用了900 eV的能量截止和0.23 Å^-1的k点间距。数据集中的额外细节存储在以"dataset_"为前缀的列中。该数据集包含3074种独特的分子构型，共有268118个原子，包含的元素有氧（O）和硅（Si），包含的性质有能量、原子力和柯西应力。

The Silica NPJCM 2022 dataset was created for the purpose of training a machine learning interatomic potential (MLIP) for silica (SiO2). Initial DFT computations were carried out using GPAW (in combination with ASE) with LDA, PBE, and PBEsol functionals; as well as VASP with the SCAN functional. All calculations employed the projector augmented-wave method. After comparison, the SCAN functional was found to perform best, and all values were recalculated using SCAN. An energy cut-off of 900 eV and a k-spacing of 0.23 Å^-1 were used. Additional details are stored in dataset columns prepended with "dataset_". The dataset contains 3074 unique molecular configurations, with a total of 268118 atoms, including the elements oxygen (O) and silicon (Si), and properties such as energy, atomic forces, and Cauchy stress.