Machine learning approach to build a skyrmion phase diagram

F. A. GÓMEZ ALBARRACÍN; H. D. ROSALES

Congreso; SIMAFE 2021 - International Seminar on Condensed Matter Physics and Statistical Physics; 2021

Universidad de la Frontera

(charla) 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. 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, bimerons and 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, weuse these algorithms for higher temperature configurations (that have not been usedin training) and discuss the resulting phase diagram.