  Methods of computational quantum chemistry provide accurate approximations of
molecular properties crucial for computer-aided drug discovery and other areas
of chemical science. However, high computational complexity limits the
scalability of their applications. Neural network potentials (NNPs) are a
promising alternative to quantum chemistry methods, but they require large and
diverse datasets for training. This work presents a new dataset and benchmark
called $\nabla^2$DFT that is based on the nablaDFT. It contains twice as much
molecular structures, three times more conformations, new data types and tasks,
and state-of-the-art models. The dataset includes energies, forces, 17
molecular properties, Hamiltonian and overlap matrices, and a wavefunction
object. All calculations were performed at the DFT level
($\omega$B97X-D/def2-SVP) for each conformation. Moreover, $\nabla^2$DFT is the
first dataset that contains relaxation trajectories for a substantial number of
drug-like molecules. We also introduce a novel benchmark for evaluating NNPs in
molecular property prediction, Hamiltonian prediction, and conformational
optimization tasks. Finally, we propose an extendable framework for training
NNPs and implement 10 models within it.
