Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

21 June 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The 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 obtain the optimal synthesis of a MOF. A synthetic conditions finder was used to derive the experimental protocols and a microwave based high-throughput robotic platform was used for the synthesis of Al-PMOF ([H2TCPP[AlOH]2(DMF3(H2O)2)]). Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). In this work, we carried out a systematic search for the optimal reaction conditions using a microwave assisted reaction synthesis. For this search we used a genetic algorithm and we show that already in the 2nd generation we obtained conditions that give excellent crystallinity and yield close to 80% in much shorter reaction time (50 minutes). In addition, by analysing the failed and partly successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.


Metal-Organic Frameworks
Machine learning
Genetic algorithm
Nanoporous materials
Carbon capture

Supplementary materials

Supplementary Materials
Supporting information for "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"


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