Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

Meyer, Benjamin and Sawatlon, Boodsarin and Corminboeuf, Clémence and von Lilienfeld, O. Anatole and Heinen, Stefan. (2018) Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts. Chemical Science, 9 (35). pp. 7069-7077.

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The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:Heinen, Stefan Niklaus and von Lilienfeld, Anatole and Meyer, Benjamin
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Royal Society of Chemistry
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:08 Jul 2020 03:10
Deposited On:20 Aug 2019 08:38

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