Nature - Communications Chemistry

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 (Al2(OH)2TCPP) [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.

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

Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
Nency P. Domingues, Seyed Mohamad Moosavi, Leopold Talirz, Kevin Maik Jablonka, Christopher P. Ireland, Fatmah Mish Ebrahim & Berend Smit

Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
Seyed Mohamad Moosavi

Theory and Simulation of Materials (THEOS), School of Engineering (STI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud, Switzerland
Leopold Talirz

Cavendish Laboratory, School of Physical Sciences, University of Cambridge, Cambridge, UK
Fatmah Mish Ebrahim

Communications Chemistry
Volume 5, article number: 170 (2022)

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