Understanding the Magnetic Microstructure through Experiments and Machine Learning Algorithms

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.

Spectroscopic studies of Dy3+ ion doped tellurite glasses for solid state lasers and white LEDs

Rare earth ion Dy³⁺-doped tellurite glasses were synthesised in the system of (75-x)TeO₂-15Sb₂O₃-10WO₃-xDy₂O₃ (TSWD glasses). XRD and FTIR characterizations were used to find the crystalline and structural properties. The intensities of the electronic transitions and the ligand environment around the Dy³⁺ ion were determined using the Judd-Ofelt (J-O) theory on the absorption spectra of the glasses. The measured luminescence spectra exhibit intense emissions at 574 and 484nm along with less intense emissions around 662 and 751nm. Various radiative properties of the ⁴F_9/2 excited level of Dy³⁺ ion were calculated for the glasses. Decay profiles were measured to find the life times and quantum efficiencies. Yellow to blue intensity ratio (Y/B), CIE chromaticity coordinates and correlated color temperature (CCT) values are calculated using the emission spectra to evaluate the emitted light. The obtained results suggest the utility of the glasses for potential yellow laser and white LED's applications.