A Highly Oxidized Cobalt Porphyrin Dimer: Spin Coupling and Stabilization of the Four-Electron Oxidation Product

2015 ◽  
Vol 55 (3) ◽  
pp. 996-1000 ◽  
Author(s):  
Soumyajit Dey ◽  
Debangsu Sil ◽  
Sankar Prasad Rath
Keyword(s):  
Oxidation Product ◽  
Spin Coupling ◽  
Cobalt Porphyrin ◽  
Porphyrin Dimer

A Highly Oxidized Cobalt Porphyrin Dimer: Spin Coupling and Stabilization of the Four-Electron Oxidation Product

2015 ◽  
Vol 128 (3) ◽  
pp. 1008-1012 ◽  
Author(s):  
Soumyajit Dey ◽  
Debangsu Sil ◽  
Sankar Prasad Rath
Keyword(s):  
Oxidation Product ◽  
Spin Coupling ◽  
Cobalt Porphyrin ◽  
Porphyrin Dimer

Through‐Space Spin Coupling in a Silver(II) Porphyrin Dimer upon Stepwise Oxidations: Ag II ⋅⋅⋅Ag II , Ag II ⋅⋅⋅Ag III , and Ag III ⋅⋅⋅Ag III Metallophilic Interactions

2019 ◽  
Vol 25 (43) ◽  
pp. 10098-10110 ◽  
Author(s):  
Akhil Kumar Singh ◽  
Mohammad Usman ◽  
Giuseppe Sciortino ◽  
Eugenio Garribba ◽  
Sankar Prasad Rath
Keyword(s):  
Spin Coupling ◽  
Porphyrin Dimer ◽  
Metallophilic Interactions

Stepwise Oxidations in a Cofacial Copper(II) Porphyrin Dimer: Through‐Space Spin‐Coupling and Interplay between Metal and Radical Spins

2020 ◽  
Vol 26 (35) ◽  
pp. 7869-7880 ◽  
Author(s):  
Amit Kumar ◽  
Sarnali Sanfui ◽  
Giuseppe Sciortino ◽  
Jean‐Didier Maréchal ◽  
Eugenio Garribba ◽  
...  
Keyword(s):  
Spin Coupling ◽  
Porphyrin Dimer

scholarly journals Through‐Space Spin Coupling in a Silver(II) Porphyrin Dimer upon Stepwise Oxidations: Ag II ⋅⋅⋅Ag II , Ag II ⋅⋅⋅Ag III , and Ag III ⋅⋅⋅Ag III Metallophilic Interactions

2019 ◽  
Vol 25 (43) ◽  
pp. 10025-10025
Author(s):  
Akhil Kumar Singh ◽  
Mohammad Usman ◽  
Giuseppe Sciortino ◽  
Eugenio Garribba ◽  
Sankar Prasad Rath
Keyword(s):  
Spin Coupling ◽  
Porphyrin Dimer ◽  
Metallophilic Interactions

Vibrational and thermal averaging of the indirect nuclear spin-spin coupling constants of CH4, SiH4, GeH4 and SnH4

1997 ◽  
Vol 91 (5) ◽  
pp. 897-907 ◽  
Author(s):  
SHEELA KIRPEKAR ◽  
THOMAS ENEVOLDSEN ◽  
JENS ODDERSHEDE ◽  
WILLIAM RAYNES
Keyword(s):  
Nuclear Spin ◽  
Coupling Constants ◽  
Spin Coupling ◽  
Spin Coupling Constants ◽  
Spin Spin Coupling

The Interplay of Open-Shell Spin-Coupling and Jahn-Teller Distortion in Benzene Radical Cation Probed by X-ray Spectroscopy

2020 ◽  
Author(s):  
Marta L. Vidal ◽  
Michael Epshtein ◽  
Valeriu Scutelnic ◽  
Zheyue Yang ◽  
Tian Xue ◽  
...  
Keyword(s):  
High Harmonic ◽  
Open Shell ◽  
Spin Coupling ◽  
High Symmetry ◽  
Teller Distortion ◽  
Spectral Window ◽  
X Ray ◽  
Jahn Teller ◽  
X Ray Absorption ◽  
Jahn Teller Distortion

We report a theoretical investigation and elucidation of the x-ray absorption spectra of neutral benzene and of the benzene cation. The generation of the cation by multiphoton ultraviolet (UV) ionization as well as the measurement of<br>the carbon K-edge spectra of both species using a table-top high-harmonic generation (HHG) source are described in the companion experimental paper [M. Epshtein et al., J. Phys.<br>Chem. A., submitted. Available on ChemRxiv]. We show that the 1sC -> pi transition serves as a sensitive signature of the transient cation formation, as it occurs outside of the spectral window of the parent neutral species. Moreover, the presence<br>of the unpaired (spectator) electron in the pi-subshell of the cation and the high symmetry of the system result in significant differences relative to neutral benzene in the spectral features associated with the 1sC ->pi* transitions. High-level calculations using equation-of-motion coupled-cluster theory provide the interpretation of the experimental spectra and insight into the electronic structure of benzene and its cation.<br>The prominent split structure of the 1sC -> pi* band of the cation is attributed to the interplay between the coupling of the core -> pi* excitation with the unpaired electron<br>in the pi-subshell and the Jahn-Teller distortion. The calculations attribute most of<br>the splitting (~1-1.2 eV) to the spin coupling, which is visible already at the Franck-Condon structure, and estimate the additional splitting due to structural relaxation to<br>be around ~0.1-0.2 eV. These results suggest that x-ray absorption with increased resolution might be able to disentangle electronic and structural aspects of the Jahn-Teller<br>effect in benzene cation.<br>

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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