Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments

2021 ◽  
Author(s):  
Viktor Zaverkin ◽  
Julia Netz ◽  
Fabian Zills ◽  
Andreas Köhn ◽  
Johannes Kästner
Keyword(s):  
Machine Learning ◽  
Magnetic Anisotropy ◽  
Gaussian Moments

scholarly journals Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Masashi Kawaguchi ◽  
Kenji Tanabe ◽  
Keisuke Yamada ◽  
Takuya Sawa ◽  
Shun Hasegawa ◽  
...  
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Magnetic Anisotropy ◽  
Magnetic Domain ◽  
Exchange Constant ◽  
Magnetic Memory ◽  
Materials Informatics ◽  
Materials Research ◽  
Neural Network System

AbstractMachine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here, we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii–Moriya (DM) interaction and the magnetic anisotropy distribution of thin-film heterostructures, parameters that are critical in developing next-generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin-film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

TEM Study of Co/Pd and Co/Au Multilayers

1993 ◽  
Vol 51 ◽  
pp. 1036-1037
Author(s):  
A.E.M. De Veirman ◽  
F.J.G. Hakkens ◽  
W.M.J. Coene ◽  
F.J.A. den Broeder
Keyword(s):  
Magnetic Anisotropy ◽  
Ion Beam ◽  
Perpendicular Magnetic Anisotropy ◽  
Optical Recording ◽  
Magnetic Multilayer ◽  
Ion Beam Sputtering ◽  
Cross Sectional ◽  
Transmission Electron ◽  
Beam Sputtering ◽  
Substrate Temperatures

There is currently great interest in magnetic multilayer (ML) thin films (see e.g.), because they display some interesting magnetic properties. Co/Pd and Co/Au ML systems exhibit perpendicular magnetic anisotropy below certain Co layer thicknesses, which makes them candidates for applications in the field of magneto-optical recording. It has been found that the magnetic anisotropy of a particular system strongly depends on the preparation method (vapour deposition, sputtering, ion beam sputtering) as well as on the substrate, underlayer and deposition temperature. In order to get a better understanding of the correlation between microstructure and properties a thorough cross-sectional transmission electron microscopy (XTEM) study of vapour deposited Co/Pd and Co/Au (111) MLs was undertaken (for more detailed results see ref.).The Co/Pd films (with fixed Pd thickness of 2.2 nm) were deposited on mica substrates at substrate temperatures Ts of 20°C and 200°C, after prior deposition of a 100 nm Pd underlayer at 450°C.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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