Development of a Ligand Knowledge Base

1. Development of a Ligand Knowledge Base Natalie Fey Crystal Grid Workshop Southampton, 17th September 2004

2. Overview • Ligand Knowledge Base • Synergy of Database Mining and Computational Chemistry: • Part 1: How computational chemistry can add value to database mining results. • Part 2: How database mining can inform a ligand knowledge base of calculated descriptors.

3. Ligand Knowledge Base • Aims: • Collect information about ligands and their (TM) complexes: • Database mining. • Computational chemistry • Exploit networked computing and data storage resources – e-Science. • Use data: • Interpretation of observations. • Predictions for new ligands.

4. Computational Chemistry (e.g. DFT) Calculate structural and electronic parameters for known and unknown TM complexes Mine Structural Databases (e.g. CSD) Compile systematic structural information about TM complexes Ligand Knowledge Base Ligand Knowledge Base

5. Query CSD for structural pattern Main Geometry / Trends Outliers Optimised Geometries Crystal Structure and DFT agree Crystal Structure and DFT disagree Part 1: “Unusual” Geometries Automatic statistical analysis of results apply outlier criteria DFT geometry optimisation compare with crystal structures

6. Part 1: “Unusual” Geometries Crystal Structure and DFT agree Value Added Why outlier? Structure Report Comment about structure? Yes No Flag for detailed investigation Note in database, may confirm by DFT Further calculations Additional results, add to database

7. Part 1: “Unusual” Geometries Crystal Structure and DFT disagree Value Added Why? Structure Report Comment about structure? Problem with Calculation Yes No Revised Calculations Problem with Structure Crystal Structure and DFT agree Further calculations Crystal Structure and DFT disagree Flag for detailed investigation Additional results, add to database Note in database

8. Example – 4-coordinate Ruthenium • Main geometry: tetrahedral (14 structures) • 2 square-planar cases: YIMLEL, QOZMEX • YIMLEL: cis-[RuCl2(2,6-(CH3)2C6H3NC)2]

9. DFT result: Use as CSD query, any TM… SIVGAV – Pd Supported by structural arguments: short Ru(II)-Cl, Ru-CNR. correct range and geometry for Pd. Run DFT with Pd: 4-coordinate Ruthenium

10. Part 2: P-donor LKB • Range of DFT-calculated descriptors for monodentate P(III) ligands and TM complexes. • Capture steric and /-electronic properties. • Identification of suitable statistical analysis approaches: • Interpretation. • Prediction.

11. Part 2: P-donor LKB • Role of database mining: • Stage 1: Database generation. • Inform input geometries (conformational freedom). • Verification of chosen theoretical approach. • Stage 2: Database utilisation. • Supply experimental data for regression models. • Confirmation of calculated trends.

12. Stage 1 Conformers: e.g. P(o-tolyl)3 Method verification: Examples

13. Examples • Stage 2: Solid State Rh-P Distance (Rh(I), CN=4)

14. Conclusions • Synergy of approaches allows to add value to structural databases. • Computational chemistry can be used to verify solid state geometries. • Can exploit e-Science resources to add value on a large scale. • Utility of large databases for structural chemistry of transition metal complexes. • Computational requirements. • Statistical analysis.

15. Acknowledgements • Guy Orpen, Jeremy Harvey • Athanassios Tsipis, Stephanie Harris • Ralph Mansson (Southampton) • Funding: