For developing globally accurate full-dimensional of large systems, the difficulties come from the large configuration space and the high cost to obtain high-quality electronic energies at the CCSD(T) or MRCI levels. Nowadays, the CCSD(T) calculation remains too computationally demanding for systems with more than 10 heavy atoms. Solutions include the hierarchical construction scheme (HC), 208 the Δ-machine learning approach (Δ-ML), 209 or the transfer learning (TL) approach. 210 TL is based on the idea that knowledge gained from solving one problem can be applied to a different but related problem. Thus, an NN PES based on a large number of low-level points can be improved by slightly adjusting its parameters with a smaller number of high-level points.