Domingues, Nency P.Moosavi, Seyed MohamadTalirz, LeopoldJablonka, Kevin MaikIreland, Christopher P.Ebrahim, Fatmah MishSmit, Berend2023-01-022023-01-022023-01-022022-12-1010.1038/s42004-022-00785-2https://infoscience.epfl.ch/handle/20.500.14299/193549WOS:000897458000001The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al-2(OH)(2)TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.Chemistry, MultidisciplinaryChemistrymetal-organic frameworksdesignchemistryporositycapturetext::journal::journal article::research article