Recognition in the domain of molecular chirality: from noncovalent interactions to separation of enantiomers

P Peluso, B Chankvetadze - Chemical Reviews, 2022 - ACS Publications
It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in
nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules …

Transition metal catalysis controlled by hydrogen bonding in the second coordination sphere

JNH Reek, B de Bruin, S Pullen, TJ Mooibroek… - Chemical …, 2022 - ACS Publications
Transition metal catalysis is of utmost importance for the development of sustainable
processes in academia and industry. The activity and selectivity of metal complexes are …

Organic reaction mechanism classification using machine learning

J Burés, I Larrosa - Nature, 2023 - nature.com
A mechanistic understanding of catalytic organic reactions is crucial for the design of new
catalysts, modes of reactivity and the development of greener and more sustainable …

Machine learning for catalysis informatics: recent applications and prospects

T Toyao, Z Maeno, S Takakusagi, T Kamachi… - Acs …, 2019 - ACS Publications
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

A comprehensive discovery platform for organophosphorus ligands for catalysis

T Gensch, G dos Passos Gomes… - Journal of the …, 2022 - ACS Publications
The design of molecular catalysts typically involves reconciling multiple conflicting property
requirements, largely relying on human intuition and local structural searches. However, the …

Transition metal-catalyzed biaryl atropisomer synthesis via a torsional strain promoted ring-opening reaction

X Zhang, K Zhao, Z Gu - Accounts of Chemical Research, 2022 - ACS Publications
Conspectus Arising from the restricted rotation of a single bond caused by steric or
electronic effects, atropisomerism is one of the few fundamental categories for molecules to …

Predicting reaction performance in C–N cross-coupling using machine learning

DT Ahneman, JG Estrada, S Lin, SD Dreher, AG Doyle - Science, 2018 - science.org
Machine learning methods are becoming integral to scientific inquiry in numerous
disciplines. We demonstrated that machine learning can be used to predict the performance …

Autonomous discovery in the chemical sciences part I: Progress

CW Coley, NS Eyke, KF Jensen - … Chemie International Edition, 2020 - Wiley Online Library
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

Accelerated dinuclear palladium catalyst identification through unsupervised machine learning

JA Hueffel, T Sperger, I Funes-Ardoiz, JS Ward… - Science, 2021 - science.org
Although machine learning bears enormous potential to accelerate developments in
homogeneous catalysis, the frequent need for extensive experimental data can be a …

Exploiting non-covalent π interactions for catalyst design

AJ Neel, MJ Hilton, MS Sigman, FD Toste - Nature, 2017 - nature.com
Molecular recognition, binding and catalysis are often mediated by non-covalent interactions
involving aromatic functional groups. Although the relative complexity of these so-called π …