A key challenge when attempting to optimise synthesis protocols either through systematic experimentation or by training machine learning models is the high dimensionality of the information contained in each synthesis. For example, 8 unique reagent chemicals were discussed in the previous section -3 metal sources, 1 linker, and 4 solvents -meaning that to fully explore the 8dimensional chemical space alone, N 8 experiments would be required (where N is the number of quantity values tested for each variable). Even when limits on the complexity of each individual reaction are used -i.e. to contain a maximum of two metal salts and solvents -the dimensionality is only reduced to 12(𝑁 ) experiments. While theoretically this dimensionality would scale with the number of synthesis steps used, we were unable to identify meaningfully distinct groups of synthesis actions (data not shown here, for brevity) and hence did not consider the sequence as impacting the synthesis outcome.