Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF
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-t...
Saved in:
Published in | Communications chemistry Vol. 5; no. 1; pp. 170 - 8 |
---|---|
Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
10.12.2022
Nature Publishing Group Springer Nature Nature Portfolio |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 USDOE |
ISSN: | 2399-3669 2399-3669 |
DOI: | 10.1038/s42004-022-00785-2 |