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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.
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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 e-Science Properties Structure
Metal-ligand binding Tsipis, Orpen and Harvey, Dalton Trans. submitted
Metal-ligand binding 2 Database mining: correlation between: Oxidation-Reduction and M–P–X angle and P–X distance Cause: π back-bonding? Leyssens, Orpen, Peeters and Harvey, to besubmitted
He8 steric probe [Cl3PdP(Me)2(CF3)]- Metal-ligand binding: a systematic approach 61 ligands, ca. 10 calculations on each Ligand Knowledge Base
Locate unusual ligands: Map of Chemical Space NR2 OR Hal Ar R
Fey, Tsipis, Harris, Harvey, Orpen & Mansson, to besubmitted Model Building • Predict experimental data from calculated variables. • Multiple linear regression: Solid State Rh-P Distance (Rh(I), CN=4) Tolman Electronic Parameter
Adding value to the structural database Query Geometry Library for User-Defined Fragment retrieval of matching data Output of Statistical Data apply outlier criteria Outliers Fey, Harris, Harvey and Orpen, to besubmitted DFT geometry optimisation Optimised Geometries compare with crystal structures Crystal Structure and DFT agree Crystal Structure and DFT disagree
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