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.