Metaheuristic conditional neural network for harvesting skyrmionic metastable states
This work provides a systematic method for identifying complex metastable skyrmionic textures, which is crucial for advancements in topological spintronics, though it appears incremental as it builds on existing neural-network and metaheuristic approaches.
The researchers tackled the problem of identifying metastable skyrmionic states in complex potential energy surfaces by developing a metaheuristic conditional neural-network method, which successfully identified spin textures with topological charges from 1 to -13 and predicted lifetimes longer than 200 ps up to 20 K. They found that stability depends linearly on topological charge for the most stable antiskyrmions and that the number of holes in the texture inversely predicts stability.
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.