Finally, to demonstrate the benefit of this approach towards synthesis optimisation, we consider the reduction in experiments that would be required to explore the identified popular sub-regions of the synthesis space. From the clustering analysis, we identified 6 sub-regions with only 3 chemicals of interest -clusters 1, 2, 3, 6, 7, and 8 in Table , containing only a single metal salt, 2methylimidazole, and a single solvent -and a further 2 sub-regions with 4 chemicals of interest: clusters 5 and 8 containing either mixed salts or solvents. Accordingly, rather than requiring N 8 or 12(𝑁 ) experiments, full exploration would only require 6(𝑁 3 ) + 2(𝑁 4 ) ≈ 𝑁 experiments. To illustrate the extent of dimensionality reduction in real terms, the number of experiments required to explore the synthesis space are shown in Table for various values of N. In combination with the quantity distributions shown in Figure and Figure , text mining and data reduction tools demonstrated in this paper will provide excellent initial values for efficient searching of chemical synthesis space, thereby accelerating methodology refinement for a range of nanomaterials.