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

• Published in: Communications chemistry Vol. 5; no. 1; pp. 170 - 8
• Main Authors: Domingues, Nency P., Moosavi, Seyed Mohamad, Talirz, Leopold, Jablonka, Kevin Maik, Ireland, Christopher P., Ebrahim, Fatmah Mish, Smit, Berend
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.12.2022; Nature Publishing Group; Springer Nature; Nature Portfolio
• Subjects: 639/4077/4057; 639/638/298/921; 639/638/549/908; 639/638/549/973; Aluminum; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Crystallinity; Genetic algorithms; Hydrothermal reactions; Machine learning; Metal-organic frameworks; Optimization; Porphyrins; Reaction time
• ISSN: 2399-3669; 2399-3669
• DOI: 10.1038/s42004-022-00785-2

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 optimize the synthesis conditions of Al-PMOF (Al 2 (OH) 2 TCPP) [H 2 TCPP = 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. Metal-organic frameworks with desirable properties can be designed through careful choice of linker and node combinations, but achieving the synthesis of a desired MOF is complex and dependent on many experimental variables. Here, a genetic algorithm combined with experimental feedback and confirmation is used to obtain the optimal microwave-assisted synthesis conditions for a porphyrin-based aluminium MOF (Al-PMOF), achieving excellent crystallinity and a close to 80% yield in only the 2 nd generation.