Development of a Ligand Knowledge Base for Phosphorus Ligands

1. Natalie FeyCombeDay, 8 January 2004 @ University of Southampton Development of a Ligand Knowledge Base for Phosphorus Ligands

2. Overview • Introduction • Computational Approach • Statistical Analysis • Results • Challenges • Outlook

3. 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

4. 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)

5. 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

6. 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) Computational Approach

7. 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 (, ))

8. 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)]:

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

10. Principal Component Analysis

11. Principal Component Analysis

12. Principal Component Analysis (excl. mixed Halides)

13. Principal Component Analysis

14. 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

15. 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) Outlook

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