Genetic-tunneling driven energy optimizer for spin systems

TL;DR

This paper introduces a novel genetic-tunneling-driven optimization method that effectively finds ground states in complex spin systems, outperforming traditional approaches like simulated annealing, and successfully maps phase diagrams of magnetic skyrmionic systems.

Contribution

The paper presents a new optimization algorithm combining local and global search techniques, specifically tailored for complex spin systems, demonstrating superior performance over existing methods.

Findings

Successfully identified ground states in skyrmionic systems

Outperformed simulated annealing in efficiency and accuracy

Mapped phase diagrams of magnetic systems

Abstract

A long-standing and difficult problem in, e.g., condensed matter physics is how to find the ground state of a complex many-body system where the potential energy surface has a large number of local minima. Spin systems containing complex and/or topological textures, for example spin spirals or magnetic skyrmions, are prime examples of such systems. We propose here a genetic-tunneling-driven variance-controlled optimization approach, and apply it to two-dimensional magnetic skyrmionic systems. The approach combines a local energy-minimizer backend and a metaheuristic global search frontend. The algorithm is naturally concurrent, resulting in short user execution time. We find that the method performs significantly better than simulated annealing (SA). Specifically, we demonstrate that for the Pd/Fe/Ir(111) system, our method correctly and efficiently identifies the experimentally observed spin spiral, skyrmion lattice and ferromagnetic ground states as a function of 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.