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.

翻译：凝聚态物理等领域中一个长期存在的难题是如何找到势能面存在大量局部极小值的复杂多体系统的基态。包含复杂和/或拓扑纹理的自旋系统（例如自旋螺旋或磁性斯格明子）正是此类系统的典型代表。本文提出一种遗传隧穿驱动的方差控制优化方法，并将其应用于二维磁性斯格明子系统。该方法结合了局部能量最小化后端与元启发式全局搜索前端，算法具有天然并行性，显著缩短了用户执行时间。我们发现该方法性能显著优于模拟退火算法。具体而言，针对Pd/Fe/Ir(111)体系，该方法能正确且高效地识别出随外磁场变化的实验观测自旋螺旋、斯格明子晶格和铁磁基态。据我们所知，当前尚无其他优化方法成功实现此目标。我们预期该成果将为利用进化计算绘制自旋系统相图铺平道路。