Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis DOI Creative Commons
Stefan P. Schmid, Leon Schlosser, Frank Glorius

и другие.

Beilstein Journal of Organic Chemistry, Год журнала: 2024, Номер 20, С. 2280 - 2304

Опубликована: Сен. 10, 2024

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, its use for enantioselective reactions gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) been increasingly applied in chemical domain efficiently uncover hidden patterns data accelerate scientific discovery. While uptake ML organocatalysis comparably slow, two decades have showed an increased from community. This review gives overview work field organocatalysis. The starts by giving short primer on experimental chemists, before discussing application predicting selectivity organocatalytic transformations. Subsequently, we employed privileged catalysts, focusing catalyst reaction design. Concluding, give our view current challenges future directions field, drawing inspiration other domains.

Язык: Английский

SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data DOI Creative Commons
Manu Suvarna, Rubén Laplaza,

Romain Graux

и другие.

ACS Catalysis, Год журнала: 2025, Номер 15(9), С. 7296 - 7307

Опубликована: Апрель 18, 2025

Volcano plots, stemming from the Sabatier principle, visualize descriptor-performance relationships, allowing rational catalyst design. Manually drawn volcanoes originating experimental studies are potentially prone to human bias as no guidelines or metrics exist quantify goodness of fit. To address this limitation, we introduce a framework called SPOCK (systematic piecewise regression for volcanic kinetics) and validate it using data heterogeneous, homogeneous, enzymatic catalysis fit volcano-like relationships. We then generalize approach DFT-derived evaluate tool's robustness against noisy kinetic in identifying false-positive volcanoes, i.e., cases where claim relationship exists, but such correlations not statistically significant. Once SPOCK's functional features established, demonstrate its potential identify exemplified via ceria-promoted water-gas shift single-atom-catalyzed electrocatalytic carbon dioxide reduction reactions. In both cases, model uncovers descriptors previously unreported, revealing insights that easily recognized by experts. Finally, showcase capabilities formulate multivariable descriptors, an emerging topic research. Our work pioneers automated standardized tool volcano plot construction validation, release open-source web application greater accessibility knowledge generation catalysis.

Язык: Английский

Процитировано

1
Augmenting Genetic Algorithms with Machine Learning for Inverse Molecular Design DOI Creative Commons
Hannes Kneiding, David Balcells

Chemical Science, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Evolutionary and machine learning methods have been successfully applied to the generation of molecules materials exhibiting desired properties. The combination these two paradigms in inverse design tasks can yield powerful that explore massive chemical spaces more efficiently, improving quality generated compounds. However, such synergistic approaches are still an incipient area research appear underexplored literature. This perspective covers different ways incorporating into evolutionary frameworks, with overall goal increasing optimization efficiency genetic algorithms. In particular, surrogate models for faster fitness function evaluation, discriminator control population diversity on-the-fly, based crossover operations, evolution latent space discussed. further potential generative is also assessed, outlining promising directions future developments.

Язык: Английский

Процитировано

5
Beyond Predefined Ligand Libraries: A Genetic Algorithm Approach for De Novo Discovery of Catalysts for the Suzuki Coupling Reactions DOI Creative Commons
Julius Seumer, Jan H. Jensen

Опубликована: Май 13, 2024

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

Язык: Английский

Процитировано

4
Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms DOI Creative Commons

Magnus Strandgaard,

Julius Seumer, Jan H. Jensen

и другие.

Chemical Science, Год журнала: 2024, Номер 15(27), С. 10638 - 10650

Опубликована: Янв. 1, 2024

Using genetic algorithms and semiempirical quantum mechanical methods for discovery of nitrogen fixation catalysts.

Язык: Английский

Процитировано

3
Beyond predefined ligand libraries: a genetic algorithm approach for de novo discovery of catalysts for the Suzuki coupling reactions DOI Creative Commons
Julius Seumer, Jan H. Jensen

PeerJ Physical Chemistry, Год журнала: 2025, Номер 7, С. e34 - e34

Опубликована: Янв. 6, 2025

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

Язык: Английский

Процитировано

0
AI Approaches to Homogeneous Catalysis with Transition Metal Complexes DOI Creative Commons
Lucía Morán‐González, Arron L. Burnage, Ainara Nova

и другие.

ACS Catalysis, Год журнала: 2025, Номер unknown, С. 9089 - 9105

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

0
One-Pot Multisubstrate Screening for Asymmetric Catalysis Enabled by 19F NMR-Based Simultaneous Chiral Analysis DOI
Donghun Kim,

G. M. Choi,

Hyunwoo Kim

и другие.

Journal of the American Chemical Society, Год журнала: 2025, Номер unknown

Опубликована: Май 27, 2025

Exploring a diverse chemical space is essential for advancing asymmetric synthesis. The complexity of the chirality-determining processes often requires resource-intensive optimization campaigns across multiple substrates. Although one-pot multisubstrate screening offers promising solution high-throughput (HTS), persistent challenge lies in accurate and efficient analysis complex reaction mixtures, which has traditionally relied on chromatography-based techniques with limited resolution. In this work, we present rapid workflow that utilizes 19F NMR spectroscopy simultaneous chiral analysis. By employing an NMR-shifting cobalt reagent, accurately determined both yields enantiomeric excesses up to 21 different substrates single mixture during ruthenium-catalyzed reductive amination ketones. This method facilitates precise through dynamic peak shifts splitting spectra induced by shift reagent. approach accelerates evaluation structure-selectivity relationships, offering valuable mechanistic insights into enantio-determining processes.

Язык: Английский

Процитировано

0
Beyond Predefined Ligand Libraries: A Genetic Algorithm Approach for De Novo Discovery of Catalysts for the Suzuki Coupling Reactions DOI Creative Commons
Julius Seumer, Jan H. Jensen

Опубликована: Фев. 14, 2024

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

Язык: Английский

Процитировано

2
Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis DOI Creative Commons
Stefan P. Schmid, Leon Schlosser, Frank Glorius

и другие.

Beilstein Journal of Organic Chemistry, Год журнала: 2024, Номер 20, С. 2280 - 2304

Опубликована: Сен. 10, 2024

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, its use for enantioselective reactions gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) been increasingly applied in chemical domain efficiently uncover hidden patterns data accelerate scientific discovery. While uptake ML organocatalysis comparably slow, two decades have showed an increased from community. This review gives overview work field organocatalysis. The starts by giving short primer on experimental chemists, before discussing application predicting selectivity organocatalytic transformations. Subsequently, we employed privileged catalysts, focusing catalyst reaction design. Concluding, give our view current challenges future directions field, drawing inspiration other domains.

Язык: Английский

Процитировано

2