Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts
Benjamin Meyer,Boodsarin Sawatlon,Stefan Heinen,Stefan Heinen,O. Anatole von Lilienfeld,O. Anatole von Lilienfeld,Clémence Corminboeuf +6 more
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The application of modern machine learning to challenges in atomistic simulation is gaining attraction and the potential for innovation in this area is being explored.Abstract:
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.read more
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Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and Modeling
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Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost
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Surface Effect of the MgCl2 Support in Ziegler–Natta Catalyst for Ethylene Polymerization: A Computational Study
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An In‐Depth Mechanistic Study of Ru‐Catalysed Aqueous Methanol Dehydrogenation and Prospects for Future Catalyst Design
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TL;DR: In this paper, the authors provide mechanistic insights into the dehydrogenation of aqueous methanol catalysed by the [Ru(trop2dae)] complex, which is in-situ generated from [Ru (trop2dad)], trop2dad=1,4−bis(5H−dibenzo[a,d]cyclohepten]-5yl)‐1, 4−diazabuta‐1-3-diene.
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