The HiFi data is determined by CC, performed at only four (stationary) points. In previous work, a series of sines and cosines was fitted to LoFi data, which led to oscillations in some regions of the dihedral space. Imposing additional constraints alleviates these issues by application of TAMkinTools 31 to minimize the curvature as well as the gradient at stationary points. In addition, we used TAMkinTools to manually combine LoFi and HiFi data with different weights according to the respective fidelity. In this study, we use the mean µ of the GPR model composed of these previously determined LoFi and HiFi data. Out of the various kernels available, we test the four described in the Methods section. The RBF kernel, cf. Eq. 6, tends to show oscillations, similar to the pure Fourier approach in the original TAMkin treatment (cf. Fig. ). A counteragent is the assignment of noise to the LoFi data set. However, this type of supervised hyperparameter tuning should be avoided: on the one hand because of the manual -and therefore heuristic -input, and on the other hand because there is no knowledge about noise in the data itself. The very flexible Matérn kernel, cf. Eq. 5, is less prone to oscillations. As shown in Fig. , the Matern32 kernel leads to reasonable results. Therefore, we selected it as the standard kernel without manual hyperparameter adjustments and fixed close-to-zero noise The scaling parameter ϕ, which is determined within the MuFi modeling process, can describe how meaningful the LoFi data is for the resulting model. In this example, the scaling parameter ϕ of the linear MuFi model is 0.817, which means that the information provided by the low fidelity DFT calculations contributes with quite some information to the MuFi prediction.