Genetic-tunneling driven energy optimizer for spin systems

Abstract

Finding the ground state of complex many-body systems, such as magnetic materials containing topological textures, like skyrmions, is a fundamental and long-standing problem. We present here a genetic-tunneling-driven variance-controlled optimization method, that efficiently identifies the ground state of two-dimensional skyrmionic systems. The approach combines a local energy-minimizer backend and a metaheuristic global search frontend. The method is shown to perform significantly better than simulated annealing. Specifically, we demonstrate that for the Pd/Fe/Ir(111) system, our method correctly and efficiently identifies the experimentally observed spin spiral geometry, skyrmion lattice and ferromagnetic ground states as a function of the external magnetic field. To our knowledge, no other optimization method has until now succeeded in doing this. We envision that our findings will pave the way for evolutionary computing in mapping out phase diagrams for spin systems in general.