Effect of the electronic structure of a chelate ligand on the character of the metal-ligand bond

1972 ◽  
Vol 21 (6) ◽  
pp. 1364-1365
Author(s):  
G. M. Larin ◽  
M. E. Dyatkina
Keyword(s):  
Electronic Structure ◽  
Chelate Ligand ◽  
Metal Ligand ◽  
Ligand Bond

scholarly journals Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

2020 ◽  
Author(s):  
Michael Taylor ◽  
Tzuhsiung Yang ◽  
Sean Lin ◽  
Aditya Nandy ◽  
Jon Paul Janet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Ground State ◽  
Spin States ◽  
Machine Learning Model ◽  
Metal Ligand ◽  
Ligand Bond

<p>Determination of ground-state spins of open-shell transition metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2,000 unique mononuclear Fe(II)/Fe(III) transition metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally-observed temperature-dependent geometric structure changes, by correctly assigning almost all (> 95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model. </p>

scholarly journals Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

2020 ◽  
Author(s):  
Michael Taylor ◽  
Tzuhsiung Yang ◽  
Sean Lin ◽  
Aditya Nandy ◽  
Jon Paul Janet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Ground State ◽  
Spin States ◽  
Machine Learning Model ◽  
Metal Ligand ◽  
Ligand Bond

<p>Determination of ground-state spins of open-shell transition metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2,000 unique mononuclear Fe(II)/Fe(III) transition metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally-observed temperature-dependent geometric structure changes, by correctly assigning almost all (> 95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model. </p>

The Crystal and Molecular Structures of Two Modifications of cis-Dichloro-mer-tris-(dimethylphenylphosphine)hydridoiridium(III)

1988 ◽  
Vol 41 (3) ◽  
pp. 283 ◽  
Author(s):  
GB Robertson ◽  
PA Tucker
Keyword(s):  
Heavy Atom ◽  
Molecular Structures ◽  
Monoclinic Space Group ◽  
X Ray Diffraction ◽  
Orthorhombic Space Group ◽  
Space Group ◽  
X Ray ◽  
Bond Lengths ◽  
Metal Ligand ◽  
Ligand Bond

The structures of two crystalline modifications of mer -(Pme2Ph)3H-cis-Cl2IrIII, (1), have been determined from single-crystal X-ray diffraction data. Modification (A) is monoclinic, space group P21/c with a 12.635(1), b 30.605(3), c 14.992(2)Ǻ, β 110.01(2)° and Z = 8. Modification (B) is orthorhombic, space group Pbca with a 27.646(3), b 11.366(1), c 17.252(2)Ǻ and Z = 8. The structures were solved by conventional heavy atom techniques and refined by full-matrix least- squares analyses to conventional R values of 0.037 [(A), 8845 independent reflections] and 0.028 [(B), 5291 independent reflections]. Important bond lengths [Ǻ] are Ir -P(trans to Cl ) 2.249(1) av. (A) and 2.234(1) (B), Ir -P(trans to PMe2Ph) 2.339(2) av. (A) and 2.344(1), 2.352(1) (B), Ir-Cl (trans to H) 2.492(2), 2.518(2) (A) and 2.503(1) (B) and Ir-Cl (trans to PMe2Ph)2.452(2) av. (A) and 2.449(1)(B). Differences in chemically equivalent metal- ligand bond lengths emphasize the importance of non-bonded contacts in determining those lengths.

Thermochemical Aspects of Organotransition Metal Chemistry. Insights Provided by Metal-Ligand Bond Enthalpies

1990 ◽  
pp. 113-125 ◽  
Author(s):  
Michel R. Gagne ◽  
Steven P. Nolan ◽  
Afif M. Seyam ◽  
David Stern ◽  
Tobin J. Marks
Keyword(s):  
Metal Chemistry ◽  
Metal Ligand ◽  
Ligand Bond

Role of Metal–Ligand Bond Strength and Phase Separation on the Mechanical Properties of Metallopolymer Films

2013 ◽  
Vol 46 (14) ◽  
pp. 5416-5422 ◽  
Author(s):  
Aaron C. Jackson ◽  
Frederick L. Beyer ◽  
Samuel C. Price ◽  
B. Christopher Rinderspacher ◽  
Robert H. Lambeth
Keyword(s):  
Mechanical Properties ◽  
Phase Separation ◽  
Bond Strength ◽  
Metal Ligand ◽  
Ligand Bond

Thermodynamics of metal-ligand bond formation. XII. Adducts of Monothio-β-diketone complexes with pyridine and bipyridine

1974 ◽  
Vol 27 (6) ◽  
pp. 1351 ◽  
Author(s):  
DR Dakternieks ◽  
DP Graddon
Keyword(s):  
Thermodynamic Data ◽  
Benzene Solution ◽  
Bond Formation ◽  
Enthalpies Of Formation ◽  
Calorimetric Titration ◽  
Metal Ligand ◽  
Ligand Bond

Thermodynamic data are reported for the addition of pyridine and bipyridine in benzene solution to monothio-β-diketone complexes, ML2, of nickel(11), copper(11), zinc(11) and mercury(11). NiL2 gives NiL2(py)2 and NiL(bpy); ZnL2 gives ZnL2(py) and ZnL2(bpy); in both cases the data show that bipyridine is bidentate. CuL2 gives CuL2 (py) and CuL2 (bpy), with almost equal enthalpies of formation, but the higher stability of CuL2(bpy) shows bipyridine is probably bidentate. HgL2 gives HgL2(py) and a reaction with bipyridine which shows that an extremely unstable adduct is formed. All data were obtained by calorimetric titration.

Thermodynamics of metal-ligand bond formation

1977 ◽  
Vol 132 (1) ◽  
pp. 1-7 ◽  
Author(s):  
D.P. Graddon ◽  
J. Mondal
Keyword(s):  
Bond Formation ◽  
Metal Ligand ◽  
Ligand Bond

Towards synergy between spin-crossover and metal–ligand bond break in molecular crystals: structural investigations of eight seven-coordinated Fe(ii) macrocyclic complexes

CrystEngComm ◽  
2015 ◽  
Vol 17 (22) ◽  
pp. 4075-4079 ◽  
Author(s):  
Hongfeng Wang ◽  
Arnaud Grosjean ◽  
Chiara Sinito ◽  
Abdellah Kaiba ◽  
Chérif Baldé ◽  
...  
Keyword(s):  
Spin Crossover ◽  
Molecular Crystals ◽  
Macrocyclic Complexes ◽  
Structural Investigations ◽  
Metal Ligand ◽  
Ligand Bond
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