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Machine learning dihydrogen activation in the chemical space surrounding Vaska’s complex

Friederich, P.; Dos Passos Gomes, G.; De Bin, R.; Aspuru-Guzik, A.; Balcells, D.

Abstract (englisch):
Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000120990
Veröffentlicht am 07.07.2020
Originalveröffentlichung
DOI: 10.1039/d0sc00445f
Scopus
Zitationen: 4
Web of Science
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2041-6520, 2041-6539
KITopen-ID: 1000120990
HGF-Programm 43.21.04 (POF III, LK 01) Molecular Engineering
Erschienen in Chemical science
Verlag Royal Society of Chemistry (RSC)
Band 11
Heft 18
Seiten 4584-4601
Vorab online veröffentlicht am 07.04.2020
Nachgewiesen in Web of Science
Scopus
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