Genetic-tunneling driven energy optimizer for spin systems

• DOI: 10.1038/s42005-023-01360-4
• Publication Date: 2023-09-02T16:01:52Z
• Authors: Qichen Xu; Zhuanglin Shen; Manuel Pereiro; Erik Sjöqvist; Pawel Herman; Olle Eriksson; Anna Delin
• Keywords: FOS: Computer and information sciences; 0301 basic medicine; 0303 health sciences; Physics; QC1-999; 05 social sciences; Computer Science - Neural and Evolutionary Computing; FOS: Physical sciences; Computational Physics (physics.comp-ph); Condensed Matter Physics; Astrophysics; 01 natural sciences; QB460-466; 0502 economics and business; Neural and Evolutionary Computing (cs.NE); 0101 mathematics; Den kondenserade materiens fysik; Physics - Computational Physics

AbstractFinding 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.