Abstract
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.
Abstract (translated)
实验中观察到的拓扑磁性纹理理论上可以通过理论计算和数值模拟预测。然而,这种计算通常受到区分局部和全局能量最小值的困难的困扰。这对允许拓扑电荷丰富的磁性材料特别有问题。通过使用传统的数值方法解决这些问题可能会非常具有挑战性,因为需要一个好的初始猜测或者需要大量的随机采样。在本研究中,我们展示了一种有效的方法,通过利用梯度下降based优化的力量,在循环神经网络与启发式搜索框架内利用,由神经网络输入的随机扰动驱动。我们通过分析 Pd/Fe/Ir(111) 系统为例展示了这种方法的力量。
URL
https://arxiv.org/abs/2303.02876
