Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Machine learning advances chemistry and materials science by enabling large-scale
exploration of chemical space based on quantum chemical calculations. While these models
supply fast and accurate predictions of atomistic chemical properties, they do not
explicitly capture the electronic degrees of freedom of a molecule, which limits their
applicability for reactive chemistry and chemical analysis. Here we present a deep learning
framework for the prediction of the quantum mechanical wavefunction in a local basis of
atomic orbitals from which all other ground-state properties can be derived. This approach
retains full access to the electronic structure via the wavefunction at force-field-like efficiency
and captures quantum mechanics in an analytically differentiable representation. On several
examples, we demonstrate that this opens promising avenues to perform inverse design of
molecular structures for targeting electronic property optimisation and a clear path towards
increased synergy of machine learning and quantum chemistry.