Machine learning approach to construct a skyrmion phase diagram

F. A. GÓMEZ ALBARRACÍN; H. D. ROSALES

Virtual

Congreso; 16 th International Seminar on Condensed Matter and Statistical Physics; 2021

Machine learning (ML) techniques have been increasingly used in condensed matter physics, particularly in identification and classification of phases. Previous works have worked in distinguishing topological skyrmion phases from helical and ferromagnetic states [1,2]. Here, we use a ML approach to study the phase diagram of a well known Heisenberg model combining ferromagnetic exchange and Dzyaloshinskii-Moriya (DM) interactions where temperature and magnetic field induce different (but similar) topological phases such as skyrmion lattices, bimeronsand skyrmion gas. We take the low temperature configurations obtained from Monte Carlo simulations for specific values of the DM couplings and train the algorithms to classify the different phases, emphasizing on the intermediate phases (bimerons and skyrmion gas), and test them by applying them to other DM values. Then, we use these algorithms for higher temperature configurations (that have not been used in training) and discuss the resulting phase diagram.Acknowledgments F.A.G.A. and H. D. R. are partially supported by CONICET and SECyT UNLP.F. A. G. A. acknowledges support from grant PICT 2018-02968[1] I.A. Iakovlev, O.M. Sotnikov, V.V. Mazurenko, Phys. Rev. B, 98, 174411, (2018).[2] V.K. Singh, J.H. Han, Phys. Rev. B, 99, 174426, (2019).