NEMO was subsequently trained on the dataset to suggest five conditions per iteration based on their highest combined EHVI. In terms of describing solvents, both the five sigma moments and the three principal components generated by applying PCA on the five sigma moments were benchmarked. XGBoost was selected as the best model for yield using three principal components with test RMSE of 3.66% and test R 2 of 0.77. When using five sigma moments, NGBoost was selected as the best model for yield with model performance of test RMSE = 3.03% and test R 2 of 0.84. Therefore, optimisation was conducted using six continuous variables and five sigma moments to describe solvents.