  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 target electronic property optimisation and a clear
path towards increased synergy of machine learning and quantum chemistry.
