On the Generality of Molecular Volcano Plots

Wodrich, Matthew D.; Sawatlon, Boodsarin; Busch, Michael; Corminbœuf, Clémence

doi:10.1002/cctc.201701709

Cited by 34 publications

(42 citation statements)

References 90 publications

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“…Figure c also shows that the clusters associated with all different ligand types reform when moving from Suzuki to Kumada coupling. This is true of species that traverse the catalytic cycle as Ni(0)/Ni(II) species (as was examined here) and does not consider the more reactive Ni(I)/Ni(III) oxidation states where the thermodynamic profiles are greatly improved . Platinum catalysts are located between Ni and Pd in terms of binding strength.…”

Section: Resultsmentioning

confidence: 99%

“…This is true of species that traverse the catalytic cycle as Ni(0)/Ni(II) species (as was examined here) and does not consider the more reactive Ni(I)/Ni(III) oxidation states [30] where the thermodynamic profiles are greatly improved. [20] Platinum catalysts are located between Ni and Pd in terms of binding strength. Despite having a number of catalysts with good thermodynamic profiles, platinum species are virtually absent from the experimental literature.…”

Section: ð2þmentioning

confidence: 99%

“…Along this line, our research group has done extensive work in crafting a computational toolkit built on volcano plots, commonly used tools for identifying attractive species in heterogeneous/electro‐catalysis, to study homogeneous reactions . Volcano plots are built upon Sabatier's principle, which states that the interaction between a catalyst and a substrate should be neither too weak nor too strong.…”

Section: Introductionmentioning

confidence: 99%

“…[13] Along this line, our research group has done extensive work in crafting a computational toolkit built on volcano plots, [14] commonly used tools for identifying attractive species in heterogeneous/electro-catalysis, [15,16] to study homogeneous reactions. [9,[17][18][19][20] Volcano plots are built upon Sabatier's principle, [21] which states that the interaction between a catalyst and a substrate should be neither too weak nor too strong. In addition to volcano plots, Sabatier's principle has also been used in other ways to understand and ultimately design better catalysts, [22] such as through the use of Kozuch and Shaik's energy span model.…”

Section: Introductionmentioning

confidence: 99%

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Data Mining the C−C Cross‐Coupling Genome

et al. 2019

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The speed and precision of machine‐learning (ML) techniques in determining quantum chemical properties has resulted in a considerable computational speed up in comparison to traditional quantum chemical methods, and now allows a desired property of thousands of molecules to be assessed virtually instantaneously. The large databases that result from employing ML can, in turn, be mined with the goal of uncovering relationships that may be missed through more commonly used small scale screening procedures. Due to its prominent place in chemistry, catalysis represents a particularly fruitful playground, where drawing connections between the quantum chemical properties of catalysts and their overall catalytic performance may lead to the identification of new, highly functional species. In this spirit, we previously trained ML models to predict the performance of 18000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we apply concepts from big data to probe a type of “C−C cross‐coupling genome” that explores results from many different named cross‐coupling reactions. The use of interactive dimensionality‐reducing data‐clustering maps facilitates the identification of relationships between the thermodynamics of different catalysts and the chemical properties of their constituent metal and ligands. Analyzing large numbers of species in this manner leads to the identification of not only unexpected catalysts that have thermodynamically ideal profiles to catalyze C−C cross‐coupling reactions, but also reveals a wealth of interesting chemical trends regarding the influence played by different metals and ligands, as well as their unique combinations.

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Section: Resultsmentioning

confidence: 99%

Section: ð2þmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Mining the C−C Cross‐Coupling Genome

et al. 2019

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“…Subsequent work has aimed at extending the applicability of these tools and has been used to construct a generalized picture of cross‐coupling reactions . However, each of these studies considered only the catalytic cycle's thermodynamic aspects and ignored kinetic considerations.…”

Section: Introductionmentioning

confidence: 99%

Expedited Screening of Active and Regioselective Catalysts for the Hydroformylation Reaction

Wodrich

Busch

Corminbœuf

2018

Helvetica Chimica Acta

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The discovery of new homogeneous catalysts that preferentially form one product over another in regio‐ or enantioselective chemical reactions has traditionally been the province of experimental chemists. Today, computational‐based approaches have carved an increasingly important role, which, for computational catalytic designs, often rely on highly inefficient combinatorial‐based screening methods. To increase the pace of discovery, tools capable of rapidly assessing large numbers of prospective species and identify those possessing desirable properties, such as activity and selectivity, are vital. Here, through the examination of the hydroformylation of 2‐methylpropene, we demonstrate how a new tool built upon molecular volcano plots can be used to quickly predict the activity of molecular catalysts as well as estimate the intrinsic ability of each species to form one regioisomer over the other with striking accuracy. Following training and validation, these regioselective molecular volcanoes are employed to predict catalysts that preferentially form the branched product (2,2‐dimethylpropanal) in violation of Keulemans’ 70‐year‐old law. Eighteen species (out of a total of 68 predicted) were computationally predicted to have regiomeric excess (r.e.) values > 90. Overall, these tools can be used to quickly screen the activity and selectivity of potential catalysis based on two easily computed descriptor variables.

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Metal‐Free Catalytic Hydrogenolysis of Silyl Triflates and Halides into Hydrosilanes**

et al. 2022

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The metal-free catalytic hydrogenolysis of silyl triflates and halides (I, Br) to hydrosilanes is unlocked by using arylborane Lewis acids as catalysts. In the presence of a nitrogen base, the catalyst acts as a Frustrated Lewis Pair (FLP) able to split H 2 and generate a boron hydride intermediate capable of reducing (pseudo)halosilanes. This metal-free organocatalytic system is competitive with metal-based catalysts and enables the formation of a variety of hydrosilanes at room temperature in high yields (> 85 %) under a low pressure of H 2 (� 10 bar).

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On the Generality of Molecular Volcano Plots

Cited by 34 publications

References 90 publications

Data Mining the C−C Cross‐Coupling Genome

Data Mining the C−C Cross‐Coupling Genome

Expedited Screening of Active and Regioselective Catalysts for the Hydroformylation Reaction

Metal‐Free Catalytic Hydrogenolysis of Silyl Triflates and Halides into Hydrosilanes**

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