We also extend the Emulator module to be compatible with optimization problems with ordinal objectives. Ordinal variables are similar to categorical parameters, except that their options have a natural ordering, but the distances between the options are unknown. For example, the diffvap crystal dataset in Olympus targets the optimization of process parameters for a vapour diffusion reaction in which the resulting product is analyzed qualitatively. The crystal score objective has 4 options (ordered from least to most desirable) : clear solution, fine powder, small crystallites, and large crystallites. We use an ordinal regression scheme to train the emulators, following closely the approach reported by Cheng. Ordinal variables with n options are encoded as binary vectors, where, for the m th option, the target vector is y = (1, 1, 1, . . . , 0, 0), where y i = 1 if i ≤ m and 0 otherwise. This encoding is such that the output of our neural network emualtor is related to cumulative probit model for ordinal regression and can be interpreted as a cumulative probability distribution on the n options. The remainder of the training protocol for the BNN emulator remains the same for ordinal regression.