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Development of a Ligand Knowledge Base Natalie Fey Crystal Grid Workshop Southampton, 17th September 2004 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. 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. Ligand Knowledge Base Mine Structural Databases (e.g. CSD) Compile systematic structural information about TM complexes Computational Chemistry (e.g. DFT) Calculate structural Ligand Knowledge and electronic parameters Base for known and unknown TM complexes Part 1: “Unusual” Geometries Query CSD for structural pattern Automatic statistical analysis of results Main Geometry / Trends apply outlier criteria Outliers DFT geometry optimisation Optimised Geometries compare with crystal structures Crystal Structure and DFT agree Crystal Structure and DFT disagree Part 1: “Unusual” Geometries Value Added Crystal Structure and DFT agree Why outlier? Structure Report Comment about structure? Yes Note in database, may confirm by DFT No Flag for detailed investigation Further calculations Additional results, add to database Part 1: “Unusual” Geometries Crystal Structure and DFT disagree Value Added Why? Structure Report Comment about structure? Revised Calculations Crystal Structure and DFT agree Note in database Problem with Calculation Yes No Problem with Structure Crystal Structure and DFT disagree Additional results, add to database Flag for detailed investigation Example – 4-coordinate Ruthenium Main geometry: tetrahedral (14 structures) 2 square-planar cases: YIMLEL, QOZMEX YIMLEL: cis-[RuCl2(2,6-(CH3)2C6H3NC)2] N N Ru Cl Cl 4-coordinate Ruthenium 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: 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. 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. Examples Stage 1 Conformers: e.g. P(o-tolyl)3 Method verification: tBu3 1.96 av. P-R, calculated 1.94 1.92 iPr3 Cy3 1.90 Et3 Pr3 Bu3 1.88 Me3 1.86 1.83 1.85 1.87 av. P-R, CSD P P 1.89 Examples Stage 2: Solid State Rh-P Distance (Rh(I), CN=4) .003 Residual predicted 2.425 2.375 2.325 0.000 -.003 2.28 2.32 2.36 Predicted Value 2.275 2.275 2.325 2.375 experimental 2.425 2.40 2.44 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. Acknowledgements Guy Orpen, Jeremy Harvey Athanassios Tsipis, Stephanie Harris Ralph Mansson (Southampton) Funding: