Before turning to the main challenges that arise from the present framework, we first address the uncertainty corresponding to the surrogate dependence on different LHS distributions. To study the sampling dependence of the GPR-surrogates, 50 surrogates were also constructed at the MR-CIS level of theory by LHS about the MECI geometries. MECI optimization was performed on each of the 50 GPR surrogate surfaces, and the mean and standard deviation of the parameters reported in Table were also determined for this dataset. For each MECI optimization, the geometry with the largest ∆E in the training set was chosen as the starting geometry in order to keep the initial conditions consistent. As expected, the surrogate dependence on LHS is also present in the MR-CIS derived surfaces, and the standard deviations on the branching space parameters is of the same order of magnitude.