The training nuclear geometries were generated using atom-centered Cartesian Latin Hypercube Sampling (LHS), and the Smooth Overlap of Atomic Positions (SOAP) descriptor was used. We found a default (see Table ) set of SOAP parameters that worked well and thse were used for all calculations. Since this work focuses on the GPR surrogate representation of DFT/MRCI(2) seams of intersection and the subsequent characterization of MECIs, we start from geometries close to the MECI and build a static surface on which a CI optimization is performed. We thus remove the requirement for active learning; namely the optimal approach for updating the surrogate and hyperparameters during the course of the MECI optimization. The employment of an active learning process will be important for the practical deployment of the approach for the optimization of structural minima, but would add additional complexity to the present proof-of-principles study and is instead reserved for future work. More Information on the SOAP parameters and initial geometries are included in the SI.