Control Group Feature Analysis. Further leveraging the holistic optimisation dataset, local performance maxima in the 5-dimensional parameter space can be identified to find conditions that significantly reduce the amount of valuable components used at a minor cost to system performance. These fruitful regions of the parameter space can be identified and visualised by reducing the five variables into a lower number of features following the recently introduced Control Group Feature analysis (herein, the two mixed features are defined as x1 and x2 in Fig S14 ; details in Methods). The approach is inspired by Buckingham analysis and based on the principle that variable combinations that offer the same mixed feature value will offer comparable performance by exploiting alternate maxima in the parameter space. This analysis highlights regions of the parameter matrix containing high performance conditions as clustered high-performing values in scatter plots.