Literature DB >> 30802036

Computational Ligand Descriptors for Catalyst Design.

Derek J Durand1, Natalie Fey1.   

Abstract

Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimization, and design of catalysts. In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,P-donor ligands, and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.

Entities:  

Year:  2019        PMID: 30802036     DOI: 10.1021/acs.chemrev.8b00588

Source DB:  PubMed          Journal:  Chem Rev        ISSN: 0009-2665            Impact factor:   60.622


  19 in total

1.  Bayesian reaction optimization as a tool for chemical synthesis.

Authors:  Benjamin J Shields; Jason Stevens; Jun Li; Marvin Parasram; Farhan Damani; Jesus I Martinez Alvarado; Jacob M Janey; Ryan P Adams; Abigail G Doyle
Journal:  Nature       Date:  2021-02-03       Impact factor: 49.962

Review 2.  Transition Metal Catalysis Controlled by Hydrogen Bonding in the Second Coordination Sphere.

Authors:  Joost N H Reek; Bas de Bruin; Sonja Pullen; Tiddo J Mooibroek; Alexander M Kluwer; Xavier Caumes
Journal:  Chem Rev       Date:  2022-05-20       Impact factor: 72.087

Review 3.  Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.

Authors:  Andrew F Zahrt; Soumitra V Athavale; Scott E Denmark
Journal:  Chem Rev       Date:  2019-12-30       Impact factor: 60.622

4.  Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis.

Authors:  Xin Yi See; Xuelan Wen; T Alexander Wheeler; Channing K Klein; Jason D Goodpaster; Benjamin R Reiner; Ian A Tonks
Journal:  ACS Catal       Date:  2020-11-05       Impact factor: 13.084

5.  Versatility and adaptative behaviour of the P^N chelating ligand MeDalphos within gold(i) π complexes.

Authors:  Miquel Navarro; Alberto Toledo; Sonia Mallet-Ladeira; E Daiann Sosa Carrizo; Karinne Miqueu; Didier Bourissou
Journal:  Chem Sci       Date:  2020-02-04       Impact factor: 9.825

6.  Data Science Meets Physical Organic Chemistry.

Authors:  Jennifer M Crawford; Cian Kingston; F Dean Toste; Matthew S Sigman
Journal:  Acc Chem Res       Date:  2021-08-05       Impact factor: 24.466

Review 7.  Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.

Authors:  Tânia F G G Cova; Alberto A C C Pais
Journal:  Front Chem       Date:  2019-11-26       Impact factor: 5.221

8.  Pyridinylidenaminophosphines: Facile Access to Highly Electron-Rich Phosphines.

Authors:  Philipp Rotering; Lukas F B Wilm; Janina A Werra; Fabian Dielmann
Journal:  Chemistry       Date:  2019-12-10       Impact factor: 5.236

9.  Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.

Authors:  Steven M Maley; Doo-Hyun Kwon; Nick Rollins; Johnathan C Stanley; Orson L Sydora; Steven M Bischof; Daniel H Ess
Journal:  Chem Sci       Date:  2020-08-21       Impact factor: 9.825

10.  Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions.

Authors:  Michael G Taylor; Tzuhsiung Yang; Sean Lin; Aditya Nandy; Jon Paul Janet; Chenru Duan; Heather J Kulik
Journal:  J Phys Chem A       Date:  2020-04-09       Impact factor: 2.781

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