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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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.
TL;DR: In this paper, a review of the development, promise, and limitations of traditional computational chemistry (i.e., force field, semi-empirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery is presented.
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Machine learning-based analysis of overall stability constants of metal–ligand complexes
TL;DR: In this paper , two Gaussian process regression models were developed to predict the first overall stability constant and the n-th (n > 1) overall stability constants of metal-ligand complexes.
Journal ArticleDOI
Scaling relationships and volcano plots of homogeneous transition metal catalysis.
Li-Cheng Yang,Xin Hong +1 more
TL;DR: The Frontier highlights the noteworthy impact of scaling relationships and volcano plots on the understanding and design of homogeneous catalysis and discusses the perspectives on the future development of this area.
Enumeration of de novo inorganic complexes for chemical discovery and machine learning
TL;DR: In this paper, the top 20% of scored ligands with density functional theory (DFT) in an octahedral homoleptic ligand database (OHLDB) were characterized.
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Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning.
TL;DR: In this article, a graph-based representation of the metal local environment for bimetallics was used for rational design of transition-metal complexes that exhibit metal-metal bonding.
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