用于采集斯格明子亚稳态的元启发式条件神经网络
Metaheuristic conditional neural network for harvesting skyrmionic metastable states
AI总结：
本文提出元启发式条件神经网络方法，结合SAM在Pd/Fe/Ir(111)体系中识别拓扑电荷从1到-13的斯格明子亚稳态，并通过自旋动力学模拟揭示其稳定性与拓扑电荷和孔洞数量的关系。
摘要：
我们提出了一种基于元启发式条件神经网络的方法，旨在识别高粗糙度势能面中具有物理意义的亚稳态。为了展示该方法的工作原理，我们在Pd/Fe/Ir(111)体系中识别并分析了拓扑电荷$Q$从1到$-13$（其中反斯格明子具有$Q<0$）的自旋织构，该体系使用基于密度泛函理论计算参数建立的经典原子自旋哈密顿量进行建模。为了便于采集相关自旋织构，我们利用了新开发的Segment Anything Model (SAM)。对于拓扑电荷$Q$从$-3$到$-6$的自旋织构，我们进一步使用有限温度自旋动力学模拟进行了分析。我们观察到，在高达约20 K的温度下，预测其寿命超过200 ps，并且当这些织构衰变时，会形成新的拓扑自旋织构。我们还发现，自旋织构的相对稳定性与拓扑电荷呈线性依赖关系，但仅在比较每个拓扑电荷下最稳定的反斯格明子时成立。总体而言，自旋织构中孔洞（即定义结构中闭合畴壁的非自相交曲线）的数量是稳定性的重要预测因子——孔洞越多，织构越不稳定。用于系统识别和表征复杂亚稳态斯格明子织构的方法——如本文所展示的方法——对于拓扑自旋电子学领域的发展具有重要意义。
英文摘要：
We present a metaheuristic conditional neural-network-based method aimed at identifying physically interesting metastable states in a potential energy surface of high rugosity. To demonstrate how this method works, we identify and analyze spin textures with topological charge $Q$ ranging from 1 to $-13$ (where antiskyrmions have $Q<0$) in the Pd/Fe/Ir(111) system, which we model using a classical atomistic spin Hamiltonian based on parameters computed from density functional theory. To facilitate the harvest of relevant spin textures, we make use of the newly developed Segment Anything Model (SAM). Spin textures with $Q$ ranging from $-3$ to $-6$ are further analyzed using finite-temperature spin-dynamics simulations. We observe that for temperatures up to around 20\,K, lifetimes longer than 200\,ps are predicted, and that when these textures decay, new topological spin textures are formed. We also find that the relative stability of the spin textures depend linearly on the topological charge, but only when comparing the most stable antiskyrmions for each topological charge. In general, the number of holes (i.e., non-self-intersecting curves that define closed domain walls in the structure) in the spin texture is an important predictor of stability -- the more holes, the less stable is the texture. Methods for systematic identification and characterization of complex metastable skyrmionic textures -- such as the one demonstrated here -- are highly relevant for advancements in the field of topological spintronics.
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