  The emergence of machine learning methods in quantum chemistry provides new
methods to revisit an old problem: Can the predictive accuracy of electronic
structure calculations be decoupled from their numerical bottlenecks? Previous
attempts to answer this question have, among other methods, given rise to
semi-empirical quantum chemistry in minimal basis representation. We present an
adaptation of the recently proposed SchNet for Orbitals (SchNOrb) deep
convolutional neural network model [Nature Commun. 10, 5024 (2019)] for
electronic wave functions in an optimised quasi-atomic minimal basis
representation. For five organic molecules ranging from 5 to 13 heavy atoms,
the model accurately predicts molecular orbital energies and wavefunctions and
provides access to derived properties for chemical bonding analysis.
Particularly for larger molecules, the model outperforms the original
atomic-orbital-based SchNOrb method in terms of accuracy and scaling. We
conclude by discussing the future potential of this approach in quantum
chemical workflows.
