These results demonstrate that there is a non-negligible sampling dependence to the resulting surrogates, regardless of the electronic structure method employed to generate the electronic energies. can also be seen more directly from Table , which shows the distribution of learned hyperparameters over the set of 50 surrogates. This error is clearly always present when fitting surfaces using training data generated from LHS distributions, and although usually small, it is particularly relevant when points of degeneracy are sought. Furthermore, while this sampling dependence is present in both the MR-CIS and DFT/MRCI(2) derived surrogates, a major difference is the value of the learned noise hyperparameter σ , it is clear that the learned DFT/MRCI(2) ω(R) and c z 0 (R) surface is a lot noisier compared to MR-CIS due to the local energetic variations. This is not unexpected, since each nuclear configuration results in the generation of a new reference space, but also highlights the need for surrogate representations of the nascent DFT/MRCI(2) surfaces in order for them to be meaningfully differentiable.