Iteratively varying parameters changes the value of each figure of merit, and thereby enables the objective function to quantify the change in overall performance. However, a complex system has too many combinatorial possibilities to test them all manually, for example, testing 10 concentrations of just 5 components gives 10 5 possibilities. Finding the maximum in such a large parameter matrix could be addressed with 'brute force' high-throughput approaches to rapidly screen all possibilities until a positive result is found. Alternatively, a learning algorithm can use Bayesian optimisation to decipher how each variable affects overall system performance and vastly reduce the number of experiments required to maximise the objective function. Because learning algorithms change multiple parameters simultaneously and capture the relationships between variables, they can reduce the number of experiments required to optimise a complex system into range for human experimentalists. Thereby, use of learning algorithms enables all laboratories to holistically optimise their systems without expensive robotic systems. When robotic systems become more commonplace in the future, they will be guided by learning algorithms to further enhance the rate of scientific discovery. Supramolecular assembly is a frontier in molecular photocatalysis and biomimicry as it can accelerate charge transfer processes and eliminate diffusion limitations by providing optimal environments for reactions, in particular photocatalytic CO2 reduction. While judicial design of molecular components can enable self-assembly such as through amphiphilicity as used herein, the dependence of assembly on the components and environment results in a wide parameter space to explore and provides ideal systems to demonstrate holistic optimisation of complex systems.