Transfer Learning for Predictive Molecular Simulations: Data-Efficient Potential Energy Surfaces at CCSD(T) Accuracy

Accurate potential energy surfaces (PESs) are critical
for predictive
molecular simulations. However, obtaining a PES at the highest levels
of quantum chemical accuracy, such as CCSD(T), becomes computationally
infeasible as molecular size increases. This work presents CCSD(T)-quality
PESs using data-efficient techniques based on transfer learning to
obtain state-of-the-art accuracy at a fraction of the computational
cost for systems that would otherwise be intractable. Most importantly,
the framework for accurate molecular simulations pursued here extends
beyond specific observables and follows a rational strategy to obtain
highest-accuracy PESs, which can be used for applications to spectroscopy
and other experiments. As rigorous tests of the PESs, semiclassical
tunnelling splittings for tropolone and the (propiolic acid)–(formic
acid) dimer (PFD) as well as anharmonic frequencies for tropolone
were determined. For tropolone, all observables are in excellent agreement
with the experiment using the high-level PES, whereas for PFD, the
agreement is less good but still orders of magnitude better than previous
calculations.