However, standard optimisation showed that the system could not be optimised for both high TOFCO and high CO yield as they appeared to require opposite conditions. Therefore, we established a novel approach and defined a holist metric set that encompassed overall system performance and which was optimised using machine learning algorithms to simultaneously improve CO yield (4.2-fold), TONCO (57%), TOFCO (47%) and quantum yield (3.8-fold), while maintaining 87% CO selectivity over H2. Machine learning enabled this optimisation in 103 tests compared to 10 5 combinatorial possibilities, thereby bringing multivariable multi-metric optimisation into for all chemistry laboratories without expensive robotic or highthroughput setups.