nabla²DFT is a large-scale dataset and benchmark in computational quantum chemistry that is designed to support machine learning models for predicting molecular electronic structure properties. Per molecule, it contains quantum-level properties like total electronic energy, DFT Hamilton matrices, forces, overlap matrices, etcetera. In addition to the data, it also contains benchmark tasks. Can be used to train neural network potentials.