Topological magnetic textures observed in experiments can, in principle, be predicted by theoretical calculations and numerical simulations. However, such calculations are, in general, hampered by difficulties in distinguishing between local and global energy minima. This becomes particularly problematic for magnetic materials that allow for a multitude of topological charges. Finding solutions to such problems by means of classical numerical methods can be challenging because either a good initial guess or a gigantic amount of random sampling is required. In this study, we demonstrate an efficient way to identify those metastable configurations by leveraging the power of gradient descent-based optimization within the framework of a feedforward neural network combined with a heuristic meta-search, which is driven by a random perturbation of the neural network's input. We exemplify the power of the method by an analysis of the Pd/Fe/Ir(111) system, an experimentally well characterized system.
翻译:实验中观察到的地形磁质素原则上可以通过理论计算和数字模拟来预测,然而,一般而言,这种计算受到当地和全球能源微型区分困难的阻碍,对于允许多种地形学费用的磁材料来说,这特别成问题。通过传统的数值方法找到解决这些问题的办法可能具有挑战性,因为要么需要良好的初步猜测,要么需要大量的随机抽样。在这项研究中,我们展示了一种有效的方法,通过在进料神经网络框架内利用梯度下沉优化的力量,再加上由神经网络输入的随机扰动驱动的超强元研究,来查明这些元性配置。我们通过分析Pd/Fe/Ir(111)系统,我们通过对实验性特征良好的系统——Pd/Fe/Ir(111)系统的分析,展示了这种方法的力量。</s>