Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**

Seumer, Julius; Hansen, Jonathan Kirschner Solberg; Nielsen, Mogens Brøndsted; Jensen, Jan H.

doi:10.1002/ange.202218565

Cited by 4 publications

(7 citation statements)

References 30 publications

(66 reference statements)

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“…To test the meta-MD based reaction discovery method described above we set out to rediscover tertiary amines and phosphines as catalysts for the MBH reaction. The MBH reaction is represented by the reaction of methyl acrylate (MA) with p-nitrobenzaldehyde (pNBA) following Seumer et al 5 The task is then to find a catalyst template that can catalyze the reaction between MA and pNBA. To demonstrate this, we initially provide a list of eight possible catalyst templates (Round 1), for which reaction networks with the reactants (MA and pNBA) are grown following step 1-4 in Section 2.…”

Section: Resultsmentioning

confidence: 99%

“…In this case, no side-reactions of the network are deemed important and we score the catalyst solely based on the reactant to TS3 GFN2-xTB electronic energy barrier as done in the original study 5 . The five GA searches done by Seumer et al are now repeated with the same starting populations (100 generations, a population size of 100 and a mutation rate of 50%, details can be found in 5) .…”

Section: Results Of the Genetic Algorithm Optimizationmentioning

confidence: 99%

“…We have previously shown that the latter step can be done efficiently using a graph-based genetic algorithm, given the TS structure of the rate determining step in the mechanism. 5 Here, we present a method for the first step.…”

Section: Discussionmentioning

confidence: 99%

“…4;5 The proposed catalyst candidate was experimentally verified to be eight times more active than 1,4-diazabicyclo[2.2.2]octane (DABCO), a commonly used catalyst for the reaction. 5 Vital to these important findings is mechanistic insight into the relevant catalytic cycle. Typically, these catalytic cycles are generated by a combination of experimental work, expert knowledge and quantum chemical calculations, making this step a laborious undertaking.…”

Section: Introductionmentioning

confidence: 99%

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De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates

Rasmussen

Seumer

Jensen

2023

Preprint

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Very recently our group has demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita- Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than 1,4-diazabicyclo[2.2.2]octane (DABCO), which is commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA-procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate determining step. Thus truly de novo catalyst discovery must also include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We use the method to show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening a selection of 11 molecular templates representing common functional groups. The presented method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery

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Section: Resultsmentioning

confidence: 99%

Section: Results Of the Genetic Algorithm Optimizationmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates

Rasmussen

Seumer

Jensen

2023

Preprint

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“…[13][14][15][16][17][18] Recently, our research has introduced the de novo design of a highly efficient organic homogeneous catalyst, specifically devised for the Morita-Baylis-Hillman reaction. [19] Utilizing a genetic algorithm, we explored the unrestricted chemical space of tertiary amines, signifying a shift from traditional screening methods and restricted chemical spaces.…”

Section: Introductionmentioning

confidence: 99%

Beyond Predefined Ligand Libraries: A Genetic Algorithm Approach for De Novo Discovery of Catalysts for the Suzuki Coupling Reactions

Seumer,

Jensen

2024

Preprint

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This study introduces a novel approach for the unrestricted de novo design of transition metal catalysts, leveraging the power of genetic algorithms (GAs) and density functional theory (DFT) calculations. By focusing on the Suzuki reaction, known for its significance in forming carbon-carbon bonds, we demonstrate the effectiveness of fragment-based and graph-based genetic algorithms in identifying novel ligands for palladium-based catalytic systems. Our research highlights the capability of these algorithms to generate ligands with desired thermodynamic properties, moving beyond the restriction of enumerated chemical libraries. Limitations in the applicability of machine learning models are overcome by calculating thermodynamic properties from first principle. The inclusion of synthetic accessibility scores further refines the search, steering it towards more practically feasible ligands. Through the examination of both palladium and alternative transition metal catalysts like copper and silver, our findings reveal the algorithms' ability to uncover unique catalyst structures within the target energy range, offering insights into the electronic and steric effects necessary for effective catalysis. This work not only proves the potential of genetic algorithms in the cost-effective and scalable discovery of new catalysts but also sets the stage for future exploration beyond predefined chemical spaces, enhancing the toolkit available for catalyst design.

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Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

Casetti

Alfonso-Ramos

Coley

et al. 2023

Chemistry A European J

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Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high‐throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.

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Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**

Cited by 4 publications

References 30 publications

De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates

De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates

Beyond Predefined Ligand Libraries: A Genetic Algorithm Approach for De Novo Discovery of Catalysts for the Suzuki Coupling Reactions

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

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