Latin Hypercube sampling (LHS) was implemented separately in the three-component space to obtain 8 solvents to generate the training dataset for the NEMO algorithm. Objective of the training dataset was to provide as much information about the reaction to the algorithm as possible. This is why several iterations of sampling were repeated to then decide the best set of solvents, see Table . Similarly, LHS was implemented on six continuous variables with lower and upper bounds (Table ) as a range to be sampled from. Six conditions per solvent were selected and used for the generation of training data. The list of solvents used in the training dataset with their respective conditions for the highest and the lowest yield in each solvent is given in Table .