Bayesian reaction optimization as a tool for chemical synthesis

Nature, Journal Year: 2021, Volume and Issue: 590(7844), P. 89 - 96

Published: Feb. 3, 2021

Language: Английский

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Toward Active-Site Tailoring in Heterogeneous Catalysis by Atomically Precise Metal Nanoclusters with Crystallographic Structures

Chemical Reviews, Journal Year: 2020, Volume and Issue: 121(2), P. 567 - 648

Published: Sept. 17, 2020

Heterogeneous catalysis involves solid-state catalysts, among which metal nanoparticles occupy an important position. Unfortunately, no two from conventional synthesis are the same at atomic level, though such regular can be highly uniform nanometer level (e.g., size distribution ∼5%). In long pursuit of well-defined nanocatalysts, a recent success is atomically precise nanoclusters protected by ligands in range tens to hundreds atoms (equivalently 1–3 nm core diameter). More importantly, have been crystallographically characterized, just like protein structures enzyme catalysis. Such merge features homogeneous catalysts ligand-protected centers) and enzymes protein-encapsulated clusters few bridged ligands). The with their total available constitute new class model hold great promise fundamental research, including dependent activity, control catalytic selectivity structure surface ligands, structure–property relationships atomic-level, insights into molecular activation mechanisms, identification active sites on nanocatalysts. This Review summarizes progress utilization for These nanocluster-based enabled heterogeneous research single-atom single-electron levels. Future efforts expected achieve more exciting understanding tailoring design high activity under mild conditions.

Language: Английский

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Advances on the Merger of Electrochemistry and Transition Metal Catalysis for Organic Synthesis

Chemical Reviews, Journal Year: 2021, Volume and Issue: 122(3), P. 3180 - 3218

Published: Nov. 19, 2021

Synthetic organic electrosynthesis has grown in the past few decades by achieving many valuable transformations for synthetic chemists. Although electrocatalysis been popular improving selectivity and efficiency a wide variety of energy-related applications, last two decades, there much interest to develop conceptually novel transformations, selective functionalization, sustainable reactions. This review discusses recent advances combination electrochemistry homogeneous transition-metal catalysis synthesis. The enabling mechanistic studies are presented alongside advantages as well future directions address challenges metal-catalyzed electrosynthesis.

Language: Английский

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Cyclic (Alkyl)- and (Aryl)-(amino)carbene Coinage Metal Complexes and Their Applications

Chemical Reviews, Journal Year: 2020, Volume and Issue: 120(9), P. 4141 - 4168

Published: April 2, 2020

Cyclic (alkyl)- and (aryl)-(amino)carbenes (CAACs CAArCs) are stronger σ-donors π-acceptors than imidazol-2-ylidenes imidazolidin-2-ylidenes, the well-known N-heterocyclic carbenes (NHCs). Consequently, they form strong bonds with coinage metals stabilize both low high oxidation states. This Review shows that CAACs CAArCs have allowed for isolation of copper gold complexes were believed to be only transient intermediates. has not a better understanding mechanism known processes but also led development novel metal-catalyzed reactions. In addition their role in homogeneous catalysis, CAAC CAArC metal recently found applications medicinal chemistry, as well materials science. When possible, performance ligands compared those classical NHCs.

Language: Английский

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A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(3), P. 1205 - 1217

Published: Jan. 12, 2022

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number potential requires pruning candidate space by efficient prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library can be used to build predictive models for catalyst performance serve as blueprint novel designs. Herein we introduce kraken, discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based representative conformer ensembles. Using quantum-mechanical methods, calculated 1558 ligands, including commercially available examples, trained machine learning predict properties over 300000 new ligands. We demonstrate application kraken systematically explore organophosphorus how existing data sets catalysis accelerate ligand selection during reaction optimization.

Language: Английский

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Molecular electrostatic potential analysis: A powerful tool to interpret and predict chemical reactivity

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2022, Volume and Issue: 12(5)

Published: Feb. 6, 2022

Abstract The molecular electrostatic potential (MESP) V ( r ) data derived from a reliable quantum chemical method has been widely used for the interpretation and prediction of various aspects reactivity. A rigorous mapping MESP topology is achieved by computing both ∇ elements Hessian matrix at critical points where = 0. In topology, intra‐ inter‐molecular bonded regions show characteristic (3, −1) bond (BCPs) while electron‐rich such as lone pair π ‐bonds +3) minimum min CPs. analysis provides simple powerful technique to characterize region in system it corresponds condensed information wave function this point due nuclei electronic distribution through Coulomb's law. successfully applied explain phenomena related reactivity ‐conjugation, aromaticity, substituent effect, ligand effects, trans‐influence, redox potential, activation energy, cooperativity, noncovalent interactions, so on. parameters ∆ n , arene systems have measures effects ligands parameter assess their σ‐donating ability metal centers. Furthermore, strong predictions on intermolecular interactive behavior can be made studies. This review summarizes applications offered large variety organic, organometallic, inorganic systems. article categorized under: Molecular Statistical Mechanics > Interactions Structure Mechanism Reaction Mechanisms Catalysis Structures

Language: Английский

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 9927 - 10000

Published: July 14, 2021

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior metal-organic bond, while very tunable achieving target properties, is challenging to predict necessitates searching a wide complex space identify needles in haystacks applications. This review will focus on techniques that make high-throughput search transition-metal chemical feasible discovery with desirable properties. cover development, promise, limitations "traditional" computational chemistry (i.e., force field, semiempirical, density theory methods) as it pertains data generation inorganic molecular discovery. also discuss opportunities leveraging experimental sources. We how advances statistical modeling, artificial intelligence, multiobjective optimization, automation accelerate lead compounds rules. overall objective this showcase bringing together from diverse areas computer science have enabled rapid uncovering structure-property relationships chemistry. aim highlight unique considerations motifs bonding (e.g., variable spin oxidation state, strength/nature) set them their apart more commonly considered organic molecules. uncertainty relative scarcity motivate specific developments machine learning representations, model training, Finally, we conclude an outlook opportunity accelerated complexes.

Language: Английский

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A review of molecular representation in the age of machine learning

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2022, Volume and Issue: 12(5)

Published: Feb. 18, 2022

Abstract Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation molecules a machine‐readable format, as this is central to computational chemistry. Four classes representations are introduced: string, connection table, feature‐based, computer‐learned representations. Three most significant simplified molecular‐input line‐entry system (SMILES), International Chemical Identifier (InChI), MDL molfile, which SMILES was first successfully be used conjunction variational autoencoder (VAE) yield continuous molecules. This noteworthy because allows for efficient navigation immensely large chemical space possible Since 2018, when model type published, considerable effort has been put into developing novel improved methodologies. Most, if not all, researchers community make their easily accessible on GitHub, though discussion computation time domain applicability often overlooked. Herein, we present questions consideration future believe will VAEs even more accessible. article categorized under: Data Science > Chemoinformatics

Language: Английский

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Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis

Science, Journal Year: 2021, Volume and Issue: 374(6565), P. 301 - 308

Published: Oct. 15, 2021

Which phosphines squeeze together? Phosphine ligands coordinated to palladium and nickel are essential tools for assembling the backbones of pharmaceutical compounds. For decades, descriptors that characterize spatial bulk have helped guide phosphine optimization. However, these tend apply ideal geometries a single ligand. Newman-Stonebraker et al . introduce descriptor considers how ligand conformation might change in crowded environment. Specifically, they found minimum percentage buried volume accurately predicts when one or two particular will coordinate metal center, frequently key determinant successful catalysis. —JSY

Language: Английский

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Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

Chemical Reviews, Journal Year: 2019, Volume and Issue: 120(3), P. 1620 - 1689

Published: Dec. 30, 2019

The dawn of the 21st century has brought with it a surge research related to computer-guided approaches catalyst design. In past two decades, chemoinformatics, application informatics solve problems in chemistry, increasingly influenced prediction activity and mechanistic investigations organic reactions. advent advanced statistical machine learning methods, as well dramatic increases computational speed memory, contributed this emerging field study. This review summarizes strategies employ quantitative structure−selectivity relationships (QSSR) asymmetric catalytic coverage is structured by initially introducing basic features these methods. Subsequent topics are discussed according increasing complexity molecular representations. As most applied subfield QSSR enantioselective catalysis, local parametrization linear free energy (LFERs) along multivariate modeling techniques described first. section followed description global first which continuous chirality measures (CCM) because single parameter derived from structure molecule. Chirality codes, global, descriptors, then introduced interaction fields (MIFs), descriptor class that typically highest dimensionality. To highlight current reach transformations, comprehensive collection examples presented. When combined traditional experimental approaches, chemoinformatics holds great promise predict new structures, rationalize behavior, profoundly change way chemists discover optimize

Language: Английский

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