Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms
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Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms. / Strandgaard, Magnus; Seumer, Julius; Jensen, Jan H.
In: Chemical Science, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms
AU - Strandgaard, Magnus
AU - Seumer, Julius
AU - Jensen, Jan H.
N1 - Funding Information: This work was supported by the Independent Research Foundation Denmark (0217-00326B) and the Novo Nordisk Foundation (NNF20OC0064104). Publisher Copyright: © 2024 The Royal Society of Chemistry.
PY - 2024
Y1 - 2024
N2 - Computational discovery of organometallic catalysts that effectively catalyze nitrogen fixation is a difficult task. The complexity of the chemical reactions involved and the lack of understanding of natures enzyme catalysts raises the need for intricate computational models. In this study, we use a dataset of 91 experimentally verified ligands as starting population for a Genetic Algorithm (GA) and use this to discover molybdenum based nitrogen fixation catalyst in trigonal bipyramidal and octahedral configurations. Through evolutionary discovery with a semi-empirical quantum method driven GA and a density functional theory (DFT) based screening process, we find 3 promising catalyst candidates that are shown to effectively catalyze the first protonation step of the Schrock cycle. Synthetic accessibility (SA) scores are used to guide the GA towards reasonable ligands and the work features a description of the GA framework, including pre-screening of catalyst candidates that involves assignment of metal coordination atoms and catalyst stereoisomers. This research thus not only offers insights into the specific field of molybdenum-based catalysts for nitrogen fixation but also demonstrates the broader applicability and potential of genetic algorithms in the field of catalyst discovery and materials science.
AB - Computational discovery of organometallic catalysts that effectively catalyze nitrogen fixation is a difficult task. The complexity of the chemical reactions involved and the lack of understanding of natures enzyme catalysts raises the need for intricate computational models. In this study, we use a dataset of 91 experimentally verified ligands as starting population for a Genetic Algorithm (GA) and use this to discover molybdenum based nitrogen fixation catalyst in trigonal bipyramidal and octahedral configurations. Through evolutionary discovery with a semi-empirical quantum method driven GA and a density functional theory (DFT) based screening process, we find 3 promising catalyst candidates that are shown to effectively catalyze the first protonation step of the Schrock cycle. Synthetic accessibility (SA) scores are used to guide the GA towards reasonable ligands and the work features a description of the GA framework, including pre-screening of catalyst candidates that involves assignment of metal coordination atoms and catalyst stereoisomers. This research thus not only offers insights into the specific field of molybdenum-based catalysts for nitrogen fixation but also demonstrates the broader applicability and potential of genetic algorithms in the field of catalyst discovery and materials science.
U2 - 10.1039/d4sc02227k
DO - 10.1039/d4sc02227k
M3 - Journal article
AN - SCOPUS:85196028596
JO - Chemical Science
JF - Chemical Science
SN - 2041-6520
ER -
ID: 395720739