Using e-Science to probe structure and bonding in metal complexes: Database mining and computation Jonathan Charmant, Frederik Claeyssens, Natalie Fey, Mairi Haddow, Stephanie Harris, Jeremy Harvey, Tom Leyssens, Ralph Mansson, A. Guy Orpen and Athanassios Tsipis CombeDay 2005 Southampton

Reactivity StructureProperties e-Science

Metal-ligand binding Tsipis, Orpen and Harvey, Dalton Trans. submitted

Metal-ligand binding 2 Leyssens, Orpen, Peeters and Harvey, to be submitted Database mining: correlation between: Oxidation-Reduction and M–P–X angle and P–X distance Cause: π back-bonding?

Metal-ligand binding: a systematic approach He 8 steric probe [Cl 3 PdP(Me) 2 (CF 3 )] - 61 ligands, ca. 10 calculations on each  Ligand Knowledge Base

Map of Chemical Space NR 2 OR Hal Ar R Locate unusual ligands:

Model Building Predict experimental data from calculated variables. –Multiple linear regression: Tolman Electronic Parameter Solid State Rh-P Distance (Rh(I), CN=4) Fey, Tsipis, Harris, Harvey, Orpen & Mansson, to be submitted

retrieval of matching data apply outlier criteria DFT geometry optimisation Query Geometry Library for User-Defined Fragment Output of Statistical Data Outliers Optimised Geometries Crystal Structure and DFT agree Crystal Structure and DFT disagree compare with crystal structures Adding value to the structural database Fey, Harris, Harvey and Orpen, to be submitted

Adding value to the structural database – 2

Conclusions Structural database is full of data Data Mining already known to yield valuable insight Combine database with computation to yield more insight Probe structure and reactivity of individual species Generate ligand knowledge base Probe structural trends and outliers