For the S N Ar benchmark, there is a negative correlation between STY and E-factor, so it is possible to optimize both simultaneously. As shown in Figure , as STY decreases, E-factor increases because increasing product throughput does not result in significantly greater environmental waste. Most of the waste comes from the flow of the solvent ethanol, which does not change significantly between reaction conditions. Out of the strategies implemented in Summit, only TSEMO is inherently able to handle multiobjective problems. For the other strategies, we implement transforms that combine the objectives The objective values of each experiment suggested by TSEMO. TSEMO explores the parameter space and exploits to find conditions that give high space-time yield (>10,000 (kg m -3 h -1 ) and low E-factor (<10.0). (b) Hypervolume trajectory of the optimisation run. Hypervolume is a measure of the number of optimal trade-offs between space-time-yield (STY) and E-factor found by a given machine learning strategy; larger hypervolumes correspond with more optimal solutions. into a single value that can be optimised. Specifically, we implement Chimera, a hierarchical scalarisation transform that works with any number of objectives, and we include a method for users to specify a custom transform of a multi-objective problem to a single objective problem. An exemplary run of TSEMO on the S N Ar benchmark is shown in Figure . Figure shows the objective values of each experiment suggested by TSEMO. The optimisation begins with two random experiments suggested by Latin Hypercube Sampling (LHS), which are then followed by suggestions by TSEMO. Out of the first suggestions, only a few have optimal STY and E-factor values. With more data, TSEMO begins to select high concentrations of 1 (near 0.5 M), high equivalents of 2 (>3.5), and short residence times (30 seconds). However, a variety of temperatures give high STY and low E-factor.