KIM-Si-training-set

This is the silicon ("KIM-Si") training set used in the forthcoming NeurIPS '22 paper titled "Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties" by Shui et al. It contains a total of 16,110 atomic configurations in extended XYZ format with total potential energies computed by eight different empirical potentials for each of them. Reference energies computed using (GGA-PBE) density functional theory (DFT) in the VASP ab initio software package are provided for 2,110 of the configurations.