Recent advances in magnetism: from Skyrmions to phase recognition with machine learning

In spite of being a mature field of research studying of magnetism and magnetic materials remains one of the most exciting area in condensed matter physics. Indeed, tremendous progress in technological development over the last decades is mainly held by achievements in spintronics and related fields, stemming from pioneering works on giant and tunnel magnetoresistance and spin-transfer torque. In this talk, we discuss a general framework which allows a direct and self-contained evaluation of spin torques within microscopic theory. We further apply our findings to explore magnetization dynamics of magnetic Skyrmions (a whirling particle-like configuration of magnetization) in both anti- and ferromagnets and magnetic solitons, and provide a theoretical analysis of prototypical logical device based on such structures. In the meantime, successful applications of neural network algorithms and machine learning to big data analysis in various areas provoke the use of this methodology in condensed matter physics. In this talk, our recent findings on unsupervised learning with self-organizing maps will be discussed in the context of phase identification for magnetic materials (primarily, a model two-dimensional ferromagnet and bcc iron). We argue further integration of machine learning approach towards a better understanding of materials informatics.

Short BIO. Dmitry Yudin studied physics at Moscow Institute of Physics and Technology (Moscow, Russia) and graduated with a PhD examination at Uppsala University (Uppsala, Sweden). During his PhD he was jointly supervised by Prof. Olle Eriksson (Uppsala University, Sweden) and Prof. Mikhail Katsnelson (Radboud University Nijmegen, the Netherlands) with the strong emphasis on studying strongly correlated electronic systems, magnetism and spin-dependent phenomena. He contributed to the development of advanced numerical technique, namely dual fermion approach, which allows to generalize a very successful dynamical mean-field theory to include spacial correlations. Another line of research activity he was pursuing at the time is studying of topologically nontrivial spin textures on exotic lattices, e.g. a skyrmion propagating along the edge of kagome lattice. He then took a position as a Research Fellow at Nanyang Technological University (Singapore) and subsequently at the ITMO University (Saint Petersburg, Russia) where he is still employed as a Researcher. He is currently active in developing a unified framework for studying spin-transfer and spin-orbit torques for antiferromagnetic spintronics.