  Machine-learned potential energy surfaces (PESs) for molecules with more than
10 atoms are typically forced to use lower-level electronic structure methods
such as density functional theory and second-order Moller-Plesset perturbation
theory (MP2). While these are efficient and realistic, they fall short of the
accuracy of the "gold standard" coupled-cluster method [CCSD(T)], especially
with respect to reaction and isomerization barriers. We report a major step
forward in using a $\Delta$-machine learning method to the challenging case of
acetlyacetone, whose MP2 barrier height for H-atom transfer is low by roughly
1.5 kcal/mol relative to the CCSD(T) barrier of 3.5 kcal/mol. From a database
of 2151 LCCSD(T) energies, and training with as few as 430 energies, we obtain
a new PES with a barrier in agreement with the LCCSD(T) one. Tunneling
splittings due to H-atom transfer are calculated using this new PES, providing
improved estimates over previous ones obtained using an MP2-based PES.
