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We present results on the identification of phase transitions in ferrimagnetic GdFeCo alloys using machine learning. The approach for finding phase transitions in the system is based on the `learning by confusion scheme, which allows one to characterize phase transitions using a universal $W$-shape. By applying the `learning by confusion scheme, we obtain 2D $W$-a shaped surface that characterizes a triple phase transition point of the GdFeCo alloy. We demonstrate that our results are in the perfect agreement with the procedure of the numerical minimization of the thermodynamical potential, yet our machine-learning-based scheme has the potential to provide a speedup in the task of the phase transition identification.
We investigate the Gilbert damping parameter for rare earth (RE)-transition metal (TM) ferrimagnets over a wide temperature range. Extracted from the field-driven magnetic domain-wall mobility, the Gilbert damping parameter was as low as 0.0072 and w
We provide a macroscopic theory and experimental results for magnetic resonances of antiferromagnetically-coupled ferrimagnets. Our theory, which interpolates the dynamics of antiferromagnets and ferromagnets smoothly, can describe ferrimagnetic reso
It has been predicted that transverse spin current can propagate coherently (without dephasing) over a long distance in antiferromagnetically ordered metals. Here, we estimate the dephasing length of transverse spin current in ferrimagnetic CoGd allo
Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio c
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperatu