Geometries of low spin states of multi-centre transition metal complexes through extended broken symmetry variational Monte Carlo

2016 ◽  
Vol 145 (12) ◽  
pp. 124107 ◽  
Author(s):  
Matteo Barborini ◽  
Leonardo Guidoni
Keyword(s):  
Monte Carlo ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Broken Symmetry ◽  
Spin States ◽  
Variational Monte Carlo

Transition Metal Complexes with Sulphur Ligands, Part 151 [1]. Ligand Enforced Configurations and Low-Spin States of [FeNS4] Cores in [FeII(L)('pyS4')] Complexes with σ and σ-π Ligands (L = N2H4, pyridine, PMe3, PnPr3; 'pyS4'2- = 2,6-bis(2-mercaptophenylthiomethyl)pyridine (2-))

2001 ◽  
Vol 56 (7) ◽  
pp. 581-588 ◽  
Author(s):  
Dieter Sellmann ◽  
Nicole Blum ◽  
Frank W. Heinemann
Keyword(s):  
Nmr Spectroscopy ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Magnetic Measurements ◽  
Spin States ◽  
X Ray ◽  
Structure Determinations

The reactions of [Fe('pyS4')]2 with PMe3 , PnPr3 , N2H4 and pyridine afforded mononuclear [Fe(L)('pyS4')] complexes with L = PMe3 ( 1 ), PnPr3 (2 ), N2H4 (3) and pyridine (4). NMR spectroscopy, magnetic measurements and X-ray structure determinations revealed that all complexes exhibit frans-thiolate donors and low-spin FeII centres, irrespective of the σ-π or σ ligand character of L. In this regard, the properties of [Fe(L)('pyS4')] complexes strongly contrast with those of [Fe(L)('NHS4')] complexes ('NHS4'2- = 2 ,2 '-bis(2 -mercaptophenylthio)- diethylamine(2 -)) and indicate that the rigid py(CH2)2 entity of the 'pyS42- ligand is able to enforce trans configurations and low-spin states of complexes with [FeNS4 ] cores. In spite of their diamagnetism, confirming the absence of antibonding electrons, all complexes 1 to 4 are highly reactive and rapidly exchange their L ligands for CO to give [Fe(CO)('pyS4')]. Evidence was obtained that the oxidation of [Fe(N'-H4)('pyS4')] (3) yields the diazene complex [μ-N2 H2 {Fe('pyS4’)}2] (5).

Molecular mechanics for multiple spin states of transition metal complexes

2003 ◽  
pp. 3949 ◽  
Author(s):  
Robert J. Deeth ◽  
David L. Foulis ◽  
Benjamin J. Williams-Hubbard
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Molecular Mechanics ◽  
Transition Metal Complexes ◽  
Spin States

Quantum Monte Carlo study of porphyrin transition metal complexes

2008 ◽  
Vol 129 (8) ◽  
pp. 085103 ◽  
Author(s):  
Jun Koseki ◽  
Ryo Maezono ◽  
Masanori Tachikawa ◽  
M. D. Towler ◽  
R. J. Needs
Keyword(s):  
Monte Carlo ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Quantum Monte Carlo ◽  
Monte Carlo Study

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>

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