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The calculation of the anharmonic modes of small to medium sized moleculesfor assigning experimentally measured frequencies to the corresponding type ofmolecular motions is computationally challenging at sufficiently high levels ofquantum chemical theory. Here, a practical and affordable way to calculatecoupled-cluster quality anharmonic frequencies using second order vibrationalperturbation theory (VPT2) from machine-learned models is presented. Theapproach, referred to as "NN + VPT2", uses a high-dimensional neural network(PhysNet) to learn potential energy surfaces (PESs) at different levels oftheory from which harmonic and VPT2 frequencies can be efficiently determined.The NN + VPT2 approach is applied to eight small to medium sized molecules(H$_2$CO, trans-HONO, HCOOH, CH$_3$OH, CH$_3$CHO, CH$_3$NO$_2$, CH$_3$COOH andCH$_3$CONH$_2$) and frequencies are reported from NN-learned models at theMP2/aug-cc-pVTZ, CCSD(T)/aug-cc-pVTZ and CCSD(T)-F12/aug-cc-pVTZ-F12 levels oftheory. For the largest molecules and at the highest levels of theory, transferlearning (TL) is used to determine the necessary full-dimensional,near-equilibrium PESs. Overall, NN + VPT2 yields anharmonic frequencies towithin 20 cm$^{-1}$ of experimentally determined frequencies for close to 90 %of the modes for the highest quality PES available and to within 10 cm$^{-1}$for more than 60 % of the modes. For the MP2 PESs only around 60 % of the NN +VPT2 frequencies were within 20~cm$^{-1}$ of the experiment, with outliers upto 150 cm$^{-1}$ compared with experiment. It is also demonstrated that theapproach allows to provide correct assignments for strongly interacting modessuch as the OH bending and the OH torsional modes in formic acid monomer andthe CO-stretch and OH-bend mode in acetic acid.

MP2 Is Not Good Enough: Transfer Learning ML Models for Accurate VPT2 Frequencies