Download Using e-Science to Probe Structure and Bonding in Metal Complexes

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
Cl
P
+
Cl
Ru
Cl
P
Cl
Ru
L
L
L=
L=
N
••
N
P
E = 23.6 kcal/mol
E = 27 kcal/mol
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 be submitted
Metal-ligand binding: a
systematic approach
He8 steric probe
[Cl3PdP(Me)2(CF3)]-
61 ligands, ca. 10 calculations on each  Ligand Knowledge Base
Map of Chemical Space
Locate unusual ligands:
3
Cl3
Principal Component 2
2
(CF3)3
HCl2
(C6F5)3
H2Cl
(3,5-CF3Ph)2
MeCl2
Ph2Me
O
P O
O
Me
1
Me(CF3)2
H3
H2F
F3
(o-tol)3
(p-CF3Ph)3
(pyr)3 (NC4H4)
Me2Cl
Me2CF3
PhPyr2
(OCH2)3CMe
Ph2Pyr
Ph(o-tol)2
(p-ClPh)3
(o-OMePh)3
Ph(o-(OMe)Ph)2
Ph2(o-tol)
Ph2(o-(OMe)Ph)
(p-FPh)3
Ph3
Ph2Cy
(m-tol)3
PhCy2
(p-MePh)3
(p-MeOPh)3
Ph2Et
0
(p-Me2NPh)3
PhMe2
Bz3
PhEt2
(OPh)3
HF2
tBu3
NR2
tBu2Me
adamphos
MeF2
Me2F
-1
(OEt)3
(OMe)3
(NH2)3
OR
Bu3
Et3
iPr3Cy3
Pr3
tBuMe2
asym
Me3
phobane
(NMe2)3
(Pip)3 (NC5H10)
Hal
Ar
(NC4H8)3
-2
R
-3
-2
-1
0
1
Principal Component 1
2
3
Model Building
Fey, Tsipis, Harris, Harvey, Orpen
& Mansson, to be submitted
• Predict experimental data from calculated variables.
– Multiple linear regression:
Solid State Rh-P Distance (Rh(I), CN=4)
Tolman Electronic Parameter
2120
2.425
2110
predicted
predicted
2100
2090
2080
2.375
2.325
2070
2060
2050
2050
2.275
2060
2070
2080
2090
experimental
2100
2110
2120
2.275
2.325
2.375
experimental
2.425
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 be submitted
DFT geometry
optimisation
Optimised Geometries
compare with
crystal structures
Crystal Structure
and DFT agree
Crystal Structure
and DFT disagree
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