Understanding the Magnetic Microstructure through Experiments and Machine Learning Algorithms
Talapatra, Abhishek (Indian Institute of Technology Hyderabad. Department of Physics)
Gajera, Udaykumar (University of Turin. Chemistry Department)
Prasad, Syam (Indian Institute of Technology Hyderabad. Department of Physics)
Arout Chelvane, Jeyaramane (Defence Metallurgical Research Laboratory (India))
Mohanty, Jyoti Ranjan (Indian Institute of Technology Hyderabad. Department of Physics)

Date:	2022
Abstract:	Advanced machine learning techniques have unfurled their applications in various interdisciplinary areas of research and development. This paper highlights the use of image regression algorithms based on advanced neural networks to understand the magnetic properties directly from the magnetic microstructure. In this study, Co/Pd multilayers have been chosen as a reference material system that displays maze-like magnetic domains in pristine conditions. Irradiation of Ar+ ions with two different energies (50 and 100 keV) at various fluences was used as an external perturbation to investigate the modification of magnetic and structural properties from a state of perpendicular magnetic anisotropy to the vicinity of the spin reorientation transition. Magnetic force microscopy revealed domain fragmentation with a smaller periodicity and weaker magnetic contrast up to the fluence of 1014 ions/cm2. Further increases in the ion fluence result in the formation of feather-like domains with a variation in local magnetization distribution. The experimental results were complemented with micromagnetic simulations, where the variations of effective magnetic anisotropy and exchange constant result in qualitatively similar changes in magnetic domains, as observed experimentally. Importantly, a set of 960 simulated domain images was generated to train, validate, and test the convolutional neural network (CNN) that predicts the magnetic properties directly from the domain images with a high level of accuracy (maximum 93. 9%). Our work has immense importance in promoting the applications of image regression methods through the CNN in understanding integral magnetic properties obtained from the microscopic features subject to change under external perturbations.
Grants:	European Commission 861145
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Published in:	ACS applied materials & interfaces, Vol. 14, Issue 44 (October 2022) , p. 50318-50330, ISSN 1944-8252

DOI: 10.1021/acsami.2c12848
PMID: 36269322

13 p, 13.4 MB

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