scholarly journals Linking the Character of the Metal–Ligand Bond to the Ligand NMR Shielding in Transition-Metal Complexes: NMR Contributions from Spin–Orbit Coupling

2017 ◽  
Vol 13 (8) ◽  
pp. 3586-3601 ◽  
Author(s):  
Jan Novotný ◽  
Jan Vícha ◽  
Pankaj L. Bora ◽  
Michal Repisky ◽  
Michal Straka ◽  
...  
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Orbit Coupling ◽  
Spin Orbit Coupling ◽  
Spin Orbit ◽  
Metal Ligand ◽  
Ligand Bond

Influence of Electronic Spin and Spin–Orbit Coupling on Decoherence in Mononuclear Transition Metal Complexes

2014 ◽  
Vol 136 (21) ◽  
pp. 7623-7626 ◽  
Author(s):  
Michael J. Graham ◽  
Joseph M. Zadrozny ◽  
Muhandis Shiddiq ◽  
John S. Anderson ◽  
Majed S. Fataftah ◽  
...  
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Orbit Coupling ◽  
Spin Orbit Coupling ◽  
Spin Orbit ◽  
Electronic Spin

scholarly journals Behind the scenes of spin-forbidden decay pathways in transition metal complexes

2021 ◽  
Vol 23 (1) ◽  
pp. 59-81
Author(s):  
Torsha Moitra ◽  
Pijush Karak ◽  
Sayantani Chakraborty ◽  
Kenneth Ruud ◽  
Swapan Chakrabarti
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Orbit Coupling ◽  
Spin Orbit Coupling ◽  
Spin Orbit ◽  
Microscopic Origin

Understanding the microscopic origin of spin-forbidden radiative and non-radiative photophysics of transition metal complexes with reference to spin–orbit coupling.

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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