scholarly journals Dzyaloshinskii-Moriya interactions and adiabatic magnetization dynamics in molecular magnets

2004 ◽  
Vol 70 (6) ◽  
Author(s):  
H. De Raedt ◽  
S. Miyashita ◽  
K. Michielsen ◽  
M. Machida
Keyword(s):  
Molecular Magnets ◽  
Magnetization Dynamics ◽  
Adiabatic Magnetization

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.

scholarly journals Ultrafast optical manipulation of magnetic order in ferromagnetic materials

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Chuangtang Wang ◽  
Yongmin Liu
Keyword(s):  
Data Storage ◽  
Optical Switching ◽  
Ultrafast Lasers ◽  
Transport Model ◽  
Magnetic Interaction ◽  
Ferromagnetic Materials ◽  
Magnetic Storage ◽  
Magnetization Dynamics ◽  
Quarter Century ◽  
Ultrafast Optical

Abstract The interaction between ultrafast lasers and magnetic materials is an appealing topic. It not only involves interesting fundamental questions that remain inconclusive and hence need further investigation, but also has the potential to revolutionize data storage technologies because such an opto-magnetic interaction provides an ultrafast and energy-efficient means to control magnetization. Fruitful progress has been made in this area over the past quarter century. In this paper, we review the state-of-the-art experimental and theoretical studies on magnetization dynamics and switching in ferromagnetic materials that are induced by ultrafast lasers. We start by describing the physical mechanisms of ultrafast demagnetization based on different experimental observations and theoretical methods. Both the spin-flip scattering theory and the superdiffusive spin transport model will be discussed in detail. Then, we will discuss laser-induced torques and resultant magnetization dynamics in ferromagnetic materials. Recent developments of all-optical switching (AOS) of ferromagnetic materials towards ultrafast magnetic storage and memory will also be reviewed, followed by the perspectives on the challenges and future directions in this emerging area.

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