Ligand-Controlled Regiodivergent Nickel-Catalyzed Hydroaminoalkylation of Unactivated Alkenes DOI
Tianze Zhang, Shan Jiang,

Mengying Qian

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(5), P. 3458 - 3470

Published: Jan. 25, 2024

Ligand modulation of transition-metal catalysts to achieve optimal reactivity and selectivity in alkene hydrofunctionalization is a fundamental challenge synthetic organic chemistry. Hydroaminoalkylation, an atom-economical approach for alkylating amines using alkenes, particularly significant amine synthesis the pharmaceutical, agrochemical, fine chemical industries. However, existing methods usually require specific substrate combinations precise regio- stereoselectivity, which limits their practical utility. Protocols allowing regiodivergent hydroaminoalkylation from same starting materials, controlling both regiochemical stereochemical outcomes, are currently absent. Herein, we report ligand-controlled, nickel-catalyzed unactivated alkenes with

Language: Английский

Data-science driven autonomous process optimization DOI Creative Commons
Melodie Christensen, Lars P. E. Yunker,

Folarin Adedeji

et al.

Communications Chemistry, Journal Year: 2021, Volume and Issue: 4(1)

Published: Aug. 2, 2021

Autonomous process optimization involves the human intervention-free exploration of a range parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop closed-loop system for carrying out parallel autonomous experiments in batch. Upon implementation our stereoselective Suzuki-Miyaura coupling, find that definition set meaningful, broad, unbiased is most critical aspect successful optimization. Importantly, discern phosphine ligand, categorical parameter, vital determination reaction outcome. To date, parameter selection has relied on chemical intuition, potentially introducing bias into experimental design. In seeking systematic method selecting diverse ligands, strategy leverages computed molecular feature clustering. The resulting uncovers conditions selectively access desired isomer high yield.

Language: Английский

Citations

175
Emerging Trends in Cross-Coupling: Twelve-Electron-Based L1Pd(0) Catalysts, Their Mechanism of Action, and Selected Applications DOI Creative Commons
Sharbil J. Firsan,

Vilvanathan Sivakumar,

Thomas J. Colacot

et al.

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(23), P. 16983 - 17027

Published: Oct. 3, 2022

Monoligated palladium(0) species, L

Language: Английский

Citations

102
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery DOI Creative Commons
Zhengkai Tu, Thijs Stuyver,

Connor W. Coley

et al.

Chemical Science, Journal Year: 2022, Volume and Issue: 14(2), P. 226 - 244

Published: Nov. 28, 2022

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Language: Английский

Citations

77
Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands DOI

Jordan J. Dotson,

Lucy van Dijk, Jacob C. Timmerman

et al.

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 145(1), P. 110 - 121

Published: Dec. 27, 2022

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe machine learning workflow for multi-objective optimization catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through two sequential required in asymmetric synthesis an active pharmaceutical ingredient. To accomplish this, density functional theory-derived database >550 ligands constructed, designer chemical space mapping technique established. The protocol used classification methods identify catalysts, followed by linear regression model selectivity. led prediction validation significantly improved all outputs, suggesting general strategy can be readily implemented optimizations where performance is controlled

Language: Английский

Citations

70
On the use of real-world datasets for reaction yield prediction DOI Creative Commons
Mandana Saebi, Bozhao Nan, John E. Herr

et al.

Chemical Science, Journal Year: 2023, Volume and Issue: 14(19), P. 4997 - 5005

Published: Jan. 1, 2023

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such have been made available. first real-world dataset ELNs pharmaceutical company disclosed its relationship to high-throughput experimentation (HTE) described. For chemical yield predictions, task synthesis, an attributed graph neural network (AGNN) performs as well or better than best previous models on two HTE Suzuki-Miyaura Buchwald-Hartwig reactions. However, training AGNN ELN does not lead predictive model. implications using data ML-based are discussed context predictions.

Language: Английский

Citations

70
Multimetallic-Catalyzed C–C Bond-Forming Reactions: From Serendipity to Strategy DOI
Laura K. G. Ackerman, Stavros K. Kariofillis, Daniel J. Weix

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(12), P. 6596 - 6614

Published: March 13, 2023

The use of two or more metal catalysts in a reaction is powerful synthetic strategy to access complex targets efficiently and selectively from simple starting materials. While capable uniting distinct reactivities, the principles governing multimetallic catalysis are not always intuitive, making discovery optimization new reactions challenging. Here, we outline our perspective on design elements using precedent well-documented C–C bond-forming reactions. These strategies provide insight into synergy compatibility individual components reaction. Advantages limitations discussed promote further development field.

Language: Английский

Citations

54
Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis DOI
Junseok Moon, Wiktor Beker, Marta Siek

et al.

Nature Materials, Journal Year: 2023, Volume and Issue: 23(1), P. 108 - 115

Published: Nov. 2, 2023

Language: Английский

Citations

51
Accelerated chemical science with AI DOI Creative Commons
Seoin Back,

Alán Aspuru-Guzik,

Michele Ceriotti

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 3(1), P. 23 - 33

Published: Dec. 6, 2023

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, academic bodies provided.

Language: Английский

Citations

45
Embracing data science in catalysis research DOI
Manu Suvarna, Javier Pérez‐Ramírez

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(6), P. 624 - 635

Published: April 23, 2024

Language: Английский

Citations

27
Applying statistical modeling strategies to sparse datasets in synthetic chemistry DOI Creative Commons
Brittany C. Haas, Dipannita Kalyani, Matthew S. Sigman

et al.

Science Advances, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and predictive tool many optimization objectives. This review aimed tutorial those entering the area chemistry. We provide case studies to highlight considerations approaches that can be used successfully analyze datasets low data regimes, common situation encountered given experimental demands Statistical hinges on (what being modeled), descriptors (how are represented), algorithms modeled). Herein, we focus how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, turnover number) structures binned, heavily skewed, distributed) influence choice algorithm constructing chemically insightful models.

Language: Английский

Citations

3