  Instant machine learning predictions of molecular properties are desirable
for materials design, but the predictive power of the methodology is mainly
tested on well-known benchmark datasets. Here, we investigate the performance
of machine learning with kernel ridge regression (KRR) for the prediction of
molecular orbital energies on three large datasets: the standard QM9 small
organic molecules set, amino acid and dipeptide conformers, and organic
crystal-forming molecules extracted from the Cambridge Structural Database. We
focus on prediction of highest occupied molecular orbital (HOMO) energies,
computed at density-functional level of theory. Two different representations
that encode molecular structure are compared: the Coulomb matrix (CM) and the
many-body tensor representation (MBTR). We find that KRR performance depends
significantly on the chemistry of the underlying dataset and that the MBTR is
superior to the CM, predicting HOMO energies with a mean absolute error as low
as 0.09 eV. To demonstrate the power of our machine learning method, we apply
our model to structures of 10k previously unseen molecules. We gain instant
energy predictions that allow us to identify interesting molecules for future
applications.
