  Nanodiamonds have a wide range of applications including catalysis, sensing,
tribology and biomedicine. To leverage nanodiamond design via machine learning,
we introduce the new dataset ND5k, consisting of 5,089 diamondoid and
nanodiamond structures and their frontier orbital energies. ND5k structures are
optimized via tight-binding density functional theory (DFTB) and their frontier
orbital energies are computed using density functional theory (DFT) with the
PBE0 hybrid functional. We also compare recent machine learning models for
predicting frontier orbital energies for similar structures as they have been
trained on (interpolation on ND5k), and we test their abilities to extrapolate
predictions to larger structures. For both the interpolation and extrapolation
task, we find best performance using the equivariant graph neural network
PaiNN. The second best results are achieved with a message passing neural
network using a tailored set of atomic descriptors proposed here.
