  Exact exchange contributions are known to crucially affect electronic states,
which in turn govern covalent bond formation and breaking in chemical species.
Empirically averaging the exact exchange admixture over compositional degrees
of freedom, hybrid density functional approximations have been widely
successful, yet have fallen short to reach high level quantum chemistry
accuracy, primarily due to delocalization errors. We propose to `adaptify`
hybrid functionals by generating optimal admixture ratios of exact exchange on
the fly, i.e. specifically for any chemical compound, using extremely data
efficient quantum machine learning models that carry negligible overhead. The
adaptive Perdew-Burke-Ernzerhof based hybrid density functional (aPBE0) is
shown to yield atomization energies with sufficient accuracy to effectively
cure the infamous spin gap problem in open shell systems, such as carbenes.
aPBE0 further improves energetics, electron densities, and HOMO-LUMO gaps in
organic molecules drawn from the QM9 and QM7b data set. Obtained with aPBE0 in
a large basis, we present a revision of the entire QM9 data set (revQM9) with
an estimated quality vastly superior to the original containing on average,
stronger covalent binding, larger band-gaps, more localized electron densities,
and larger dipole-moments. While aPBE0 is applicable in the equilibrium regime,
outstanding limitations include covalent bond dissociation when going beyond
the Coulson-Fisher point.
