CombeDay NF

Natalie FeyCombeDay, 8 January 2004 @ University of Southampton: 

Natalie Fey CombeDay, 8 January 2004 @ University of Southampton Development of a Ligand Knowledge Base for Phosphorus Ligands

Overview: 

Overview Introduction Computational Approach Statistical Analysis Results Challenges Outlook

Introduction: 

Introduction Ligand Knowledge Base mine CSD and other databases geometry of metal complexes (bond lengths, angles, conformations) supramolecular interactions experimental data supplement by calculated data geometry, conformational freedom electronic structure transition states complexes not structurally characterised

Introduction: 

Introduction Phosphorus Ligands, PX3 (X = R, Hal, Ar, OR, OAr, NR2, mixed) widespread use as ligands in transition metal complexes tune steric and electronic properties importance in homogeneous catalysis established measures of steric and electronic properties steric: Tolman’s cone angle, solid angle, Brown’s steric parameter, Orpen’s S4’ parameter electronic: Tolman’s electronic parameter (CO), pKa, PA, IE, EB, CB, CO Tolman, Brown, QALE (Prock, Giering)

Computational Approach: 

Computational Approach Problems with TM Complexes treatment of large numbers of electrons, electron correlation geometrical effects of partially filled d-orbitals (spin states, Jahn-Teller effects) variable coordination numbers and modes suitable data for verification Density Functional Theory Jaguar, BP86/6-31G* on ligands, LACV3P on metal

Computational Approach: 

Computational Approach Phosphorus Ligands alkyl phosphines, PR3 (R = H, Me, Et, Pr, iPr, Bu, tBu, Cy, CF3, asymmetric: 1, 2, 3) aryl phosphines, PAr3 (Ar = Ph, o-tolyl, p-tolyl, p-F-Ph, p-(MeO)-Ph, p-Cl-Ph, p-(CF3)-Ph, p-(Me2N)-Ph, C6F5 , 3,5-(CF3)2-Ph), model: CH=CH2 phosphine halides, PHal3 (Hal = F, Cl) phosphites, POR3 (R = Me, Et, Ph, 4) amino phosphines, PNR2 (R = H, Me; cyclic: NC4H4, NC4H8, NC5H10) mixed halides (PH2Hal, PHHal2, PMe2Hal, PMeHal2, Hal = F, Cl, CF3 (Me only))

Computational Approach: 

Computational Approach Complexes free ligand (PX3) phosphorus ligand cation ([HPX3]+) H3B(PX3) OPX3 [(PH3)5Mo(PX3)] [Cl3Pd(PX3)]- [(PH3)3Pt(PX3)] Variables energetic: EHOMO, ELUMO, PA, BDE, He(steric) NBO charges of MLn fragments coordinated to PX3 geometrical: (P-X), (X-P-X), d(P-M), geometry of M-L fragment (cis, trans effects, L-M-L)

Statistical Analysis: 

Statistical Analysis Bivariate Correlations linear, non-linear Hierarchical Clustering identify groups by measuring distance in multi-dimensional space Principal Component Analysis reduce number of variables by formulation of principal components (linear combinations of variables which account for maximum of variation in original variables) chemical interpretation of PCs? (steric, electronic (, ))

Results: 

Results Pearson Correlations identify linearly correlated variables use to reduce number of variables fewer complexes to optimise simplify interpretation of PCs e.g. [Cl3Pd(PX3)]- and [(PH3)3Pt(PX3)]:

Results: 

Results Hierarchical Cluster (Pearson Correlation, STD=1, B & Pt data)

Principal Component Analysis (excl. mixed Halides): 

Principal Component Analysis (excl. mixed Halides)

Principal Component Analysis (excl. mixed Halides): 

Principal Component Analysis (excl. mixed Halides)

Principal Component Analysis : 

Principal Component Analysis

Principal Component Analysis : 

Principal Component Analysis

Principal Component Analysis (excl. mixed Halides): 

Principal Component Analysis (excl. mixed Halides)

Principal Component Analysis: 

Principal Component Analysis

Challenges: 

Challenges selection of complexes and variables treatment of bidentate phosphorus ligands expansion to other ligand sets chemical interpretation of principal components steric and electronic effects contribute to variables reliability of established measures (cone angles) robustness of analysis variation in ligand set and variables (high correlation) exploration of conformational space treatment of multiple minima automation of calculations, data analysis, statistical analysis eliminate data transfer mistakes reliable error behaviour

Outlook: 

Outlook started expansion of ligand sets explore model building predict experimental and calculated data from subset of variables linear, non-linear explore measures of quantum similarity (Fukui function, HSAB)

Acknowledgements: 

Acknowledgements Guy Orpen, Jeremy Harvey Athanassios Tsipis, Stephanie Harris Funding: