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 (DFT) and second-order
Møller–Plesset perturbation theory (MP2). While these
are efficient and realistic, they fall short of the accuracy of the
“gold standard” coupled-cluster method, especially with
respect to reaction and isomerization barriers. We report a major
step forward in applying a Δ-machine learning method to the
challenging case of acetylacetone, whose MP2 barrier height for H-atom
transfer is low by roughly 1.1 kcal/mol relative to the benchmark
CCSD(T) barrier of 3.2 kcal/mol. From a database of 2151 local CCSD(T)
energies and training with as few as 430 energies, we obtain a new
PES with a barrier of 3.5 kcal/mol in agreement with the LCCSD(T)
barrier of 3.5 kcal/mol and close to the benchmark value. 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.