𝑦 obj = 𝑤 1 ln ( Yield C𝑂 QY ) + 𝑤 2 ln(𝑇𝑂𝐹 𝐶𝑎𝑡 ) + 𝑤 3 ln(𝑇𝑂𝐹 𝑃𝑆 ) (Eq. 1) predictions and iteration. Two Bayesian optimisation methodologies (GpyOpt 29 and Gryffin 30 ) were used with multiple acquisition functions to control paths through promising regions of the parameter space and exploit known peaks, while balancing the need to explore new regions (exploitation vs exploration; see experimental section). Holistic optimisation proceeded through 10 iterations with each iteration providing five concentrations of catalyst, photosensitiser, surfactant, reductant, and buffer to test (Fig , values in Table ). Two optimisations were conducted in parallel by using different weightings of the objective function components, where Y obj1 considered all components with weighting coefficients w1 = 0.4, w2 = 0.4, w3 = 0.2; while y obj2 excluded the TOFCO of photosensitiser with w1 = 0.5, w2 = 0.5, w3 = 0. By optimising the two objective functions simultaneously, this provides 10 sets of conditions (optimisation paths) each iteration, which matches the capacity of the experimental equipment. The results were then all added into the dataset pool to provide all possible information to all algorithms (largest possible dataset pool at each iteration).