MuFi methods, potentially with Bayesian prediction, have reduced computational cost in chemical modeling. They have recently been applied to model PESs of small molecules like N 2 , H 2 O, formic acid and methanol. They can bridge the gap between fast lowfidelity (LoFi) and expensive high-fidelity (HiFi) methods, like Snyder et al. demonstrated for semiempirical potentials and QM/MM methods by using Gaussian process regression (GPR). This study uses MuFi methods to transfer the accuracy of HiFi wave-function theory like coupled-cluster down to efficient DFT PESs for N ×1DHR of larger molecules and complexes.