Molecular and Crystal Magnetic Engineering of Polymetallic Coupling System: From Magnetic Molecules to Molecular Magnets

2010 ◽  
Vol 19 (3) ◽  
pp. 208-221 ◽  
Author(s):  
Peng Cheng ◽  
Dai-Zheng Liao
2021 ◽  
Author(s):  
Jiapeng Ma ◽  
Yuan Yuan ◽  
Baotao Kang ◽  
Jin Yong Lee

Sufficiently strong molecular magnets are used in small modern electronic and spintronic devices. Diradical organic magnetic molecules (OMMs) are promising options due to their lightness, flexibility, and low energy required...


RSC Advances ◽  
2016 ◽  
Vol 6 (76) ◽  
pp. 72510-72518 ◽  
Author(s):  
Rebecca J. Holmberg ◽  
Ilia Korobkov ◽  
Muralee Murugesu

Extending molecular systems into chain networks is a unique method with which to orient magnetic molecules into well-ordered arrays along one dimension, and study their resulting properties.


2017 ◽  
Vol 8 (9) ◽  
pp. 6051-6059 ◽  
Author(s):  
Alessandro Lunghi ◽  
Federico Totti ◽  
Stefano Sanvito ◽  
Roberta Sessoli

The design of slow relaxing magnetic molecules requires the optimization of internal molecular vibrations to reduce spin-phonon coupling.


1906 ◽  
Vol 25 (2) ◽  
pp. 1025-1059 ◽  
Author(s):  
W. Peddie

1. The gradual growth of the theory of molecular magnetism from the original suggestions of Poisson and Weber is well known. The recent great development, made by Ewing, and tested experimentally by means of models, has placed the theory on a fairly firm basis, and has made essentially secure the fundamental postulate that magnetic phenomena in material bodies are due to magnetic molecules which may possibly be regarded as free from any directional control other than that supplied by their own mutual action.


Author(s):  
William J. Dougherty ◽  
Samuel S. Spicer

In recent years, considerable attention has focused on the morphological nature of the excitation-contraction coupling system of striated muscle. Since the study of Porter and Palade, it has become evident that the sarcoplastic reticulum (SR) and transverse tubules constitute the major elements of this system. The problem still exists, however, of determining the mechamisms by which the signal to interdigitate is presented to the thick and thin myofilaments. This problem appears to center on the movement of Ca++ions between myofilaments and SR. Recently, Philpott and Goldstein reported acid mucosubstance associated with the SR of fish branchial muscle using the colloidal thorium dioxide technique, and suggested that this material may serve to bind or release divalent cations such as Ca++. In the present study, Hale's iron solution adapted to electron microscopy was applied to formalin-fixed myofibrils isolated from glycerol-extracted rabbit psoas muscles and to frozen sections of formalin-fixed rat psoas muscles.


2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann

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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