Setting P-Donor Ligands into Context: An Application of the Ligand Knowledge Base (LKB) Approach

Fey, Natalie; Papadouli, Sofia; Pringle, Paul G.; Ficks, Arne; Fleming, James T.; Higham, Lee J.; Wallis, Jennifer F.; Carmichael, Duncan; Mézailles, Nicolas; Müller, Christian

doi:10.1080/10426507.2014.983599

Cited by 9 publications

(15 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A range of structural parameters, energies, and steric properties are extracted from these calculations (Table S5) and have been used in regression models but were also processed with a statistical projection technique, principal component analysis (PCA), to optimally represent ligand similarities as distances on a so-called map of chemical space . As shown recently for monodentate ligands, such maps, where proximity indicates ligands with similar properties, can set unusual ligand designs, including phosphinines, − , into context and so suggest possible applications in catalysis.…”

Section: Resultsmentioning

confidence: 99%

Accessing Alkyl- and Alkenylcyclopentanes from Cr-Catalyzed Ethylene Oligomerization Using 2-Phosphinophosphinine Ligands

Newland

Smith

et al. 2018

Organometallics

Self Cite

View full text Add to dashboard Cite

Desilylation of the 2-phosphinophosphinine 2-PPh 2-3-Me-6-SiMe 3-PC 5 H 2 with HCl gave 2-PPh 2-3-Me-PC 5 H 3 , demonstrating the late-stage modification of this bidentate heterocyclic ligand. Group 6 metal carbonyl complexes of these ligands showed κ 2-binding and very small bite-angles of 65.1-68.3°, and also demonstrated that the donor properties of 2phosphinophosphinines can be tuned readily by the presence of the SiMe 3 group which gives a more π-accepting phosphinine ligand. The properties of 2-phosphinophosphinines were compared to bidentate diphosphorus ligands computationally, contextualizing them in the Ligand Knowledge Base for bidentate P,P donor ligands (LKB-PP) and were found to occupy an area of ligand space adjacent to Ar 2 PN(R)NAr 2 ligands that have been successfully used in ethylene oligomerization reactions, but with well-separated properties in the second principal component. Testing 2-phosphinophosphinines in Cr-catalyzed ethylene oligomerization reactions showed key differences to standard PNP ligands in that a high proportion of alkyl-and alkenyl-cyclopentanes were formed. This demonstrates that the different donor properties of 2-phosphinophosphinines influence the reactivity of the key 7-membered metallacycle postulated in the metallacyclic reaction mechanism, generating products from isomerization and subsequent ethylene insertion. Alkyl-and alkenyl-cyclopentanes represent new products for the key industrial feedstock ethylene, with the alkenes having potential as new monomers, comonomers or additives for plastics. Computational evaluation of ligand properties and the resulting property maps can play a role in suggesting future ligand developments to change the selectivity of this industriallyrelevant system in the pursuit of new products generated from ethylene.

show abstract

Section: Resultsmentioning

confidence: 99%

Accessing Alkyl- and Alkenylcyclopentanes from Cr-Catalyzed Ethylene Oligomerization Using 2-Phosphinophosphinine Ligands

Newland

Smith

et al. 2018

Organometallics

Self Cite

View full text Add to dashboard Cite

show abstract

“…124 Ligand maps can be used in their own right to select alternative ligands and set novel designs into context, as demonstrated for fluorophosphines 106 and a range of unusual ligand designs. 97,127 Going beyond a comparison of ligand properties, such maps can also be used to explore the sampling of ligand space by a set of ligands, either, as is the case in this review, driven by commercial and data availability, or for the Design of Experiments (DoE) 56,200 and the identification of areas of ligand space that correspond to favourable catalyst performance, as described by both us 96 and others 200 for LKB-P. 96 Such applications crucially depend on the availability of suitable experimental data. Here, catalyst screening with designed ligand sets, followed by several iterations of data analysis and further screening, are perhaps most promising.…”

Section: Mapping Chemical Spacementioning

confidence: 99%

“…Some areas of ligand space are sampled more thoroughly than others (towards the right hand (Eastern) side of the map, and chemically-biased towards alkyl-and aryl-substituted ligands), which can ultimately affect the predictive performance of models, especially where models begin to extrapolate, but may also reflect chemical stability, areas that contain privileged ligands for a wide range of reactions, and indeed biases introduced by commercial availability and the preferences of many research groups. 97 The principal components shown capture 62 % of the variation. Each symbol corresponds to a ligand, and shape and colour are determined by substituents as shown in the legend.…”

Section: Mapping Chemical Spacementioning

confidence: 99%

“…Here, catalyst screening with designed ligand sets, followed by several iterations of data analysis and further screening, are perhaps most promising. 12,201 Figure 9 illustrates the distribution of the P-donor ligand set considered here on the latest published version of the LKB-P map, 98 with similar maps for LKB-PP 128 and LKB-C 167 included in the Supporting Information (Figures S1 and S2). Some areas of ligand space are sampled more thoroughly than others (toward the right-hand (eastern) side of the map, and chemically biased toward alkyl-and aryl-substituted ligands), which can ultimately affect the predictive performance of models, especially where models begin to extrapolate, but may also reflect chemical stability, areas that contain privileged ligands for a wide range of reactions, and indeed biases introduced by commercial availability and the preferences of many research groups.…”

Section: Chemical Spacementioning

confidence: 99%

See 1 more Smart Citation

Computational Ligand Descriptors for Catalyst Design

2019

Self Cite

View full text Add to dashboard Cite

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, optimisation 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,Pdonor 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.

show abstract

“…2). 27,29,35 These maps can be useful in their own right, as demonstrated in a collaboration with Paul Pringle's group concerned with the design of novel ligand structures, with a particular focus on monodentate P-donor ligands substituted by fluorine for applications in hydroformylation and hydrocyanation. 27 In this work, we were interested in assessing ligand designs before synthesis and three fluorophosphine ligands (a phosphaadamantane cage ligand (1) and two phosphabicyclic ligands (2, 3), Fig.…”

Section: Lkb-pmentioning

confidence: 99%

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Durand

Fey

2021

Acc. Chem. Res.

Self Cite

View full text Add to dashboard Cite

Computers have become closely involved with most aspects of modern life and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry -it is now entirely possible to complete research projects where computation and synthesis are cooperative, integrated, and work in synergy to achieve better insights and so improved results. It remains our ambition to put computational prediction before experiment, and we have been working towards developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centres ideal for high value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalysed reactions across the vast chemical space of different catalysts and substrates are not fully explored, nor is such an exploration feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimisation, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesised, hinting at quite complex mechanistic manifolds for organometallic catalysts which are affected by the combination of input variables. Mechanistic deviation is most likely when challenging, multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalysts libraries until a new "best" match between catalyst and substrate can be identified and reaction conditions optimised. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealised catalytic cycles, rather than working towards a "big data" view of organometallic homogeneous catalysis, with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarise here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists, so they challenge and validate quantitative computational approaches. In this account we demonstrate their application to different aspects of cataly...

show abstract

Setting P-Donor Ligands into Context: An Application of the Ligand Knowledge Base (LKB) Approach

Cited by 9 publications

References 48 publications

Accessing Alkyl- and Alkenylcyclopentanes from Cr-Catalyzed Ethylene Oligomerization Using 2-Phosphinophosphinine Ligands

Accessing Alkyl- and Alkenylcyclopentanes from Cr-Catalyzed Ethylene Oligomerization Using 2-Phosphinophosphinine Ligands

Computational Ligand Descriptors for Catalyst Design

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Contact Info

Product

Resources

About