title: nabla^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like
    Molecules and a Benchmark for Neural Network Potentials
summary: 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^2DFT 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
  (omegaB97X-D/def2-SVP) for each conformation. Moreover, nabla^2DFT 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.
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paper_title: nabla^2DFT: A Universal Quantum Chemistry Dataset of Drug-Like
    Molecules and a Benchmark for Neural Network Potentials
paper_summary: 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^2DFT 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
  (omegaB97X-D/def2-SVP) for each conformation. Moreover, nabla^2DFT 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.
paper_ai_keywords: nablaDFT Neural network potentials (NNPs) energies forces molecular properties Hamiltonian overlap matrices wavefunction object DFT level ($\omega$B97X-D/def2-SVP) relaxation trajectories conformational optimization