We present a metaheuristic conditional neural-network-based method aimed at identifying physically interest-ing 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. 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 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.

Finding the ground state of complex many-body systems, such as magnetic materials containing topological textures, like skyrmions, is a fundamental and long-standing problem. We present here a genetic-tunneling-driven variance-controlled optimization method, that efficiently identifies the ground state of two-dimensional skyrmionic systems. The approach combines a local energy-minimizer backend and a metaheuristic global search frontend. The method is shown to perform significantly better than simulated annealing. Specifically, we demonstrate that for the Pd/Fe/Ir(111) system, our method correctly and efficiently identifies the experimentally observed spin spiral geometry, skyrmion lattice and ferromagnetic ground states as a function of the 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.

Skyrmion stabilization in novel magnetic systems with the B20 crystal structure is reported here, primarily based on theoretical results. The focus is on the effect of alloying on the 3d sublattice of the B20 structure by substitution of heavier 4d and 5d elements, with the ambition to tune the spin-orbit coupling and its influence on magnetic interactions. State-of-the-art methods based on density functional theory are used to calculate both isotropic and anisotropic exchange interactions. Significant enhancement of the Dzyaloshinskii-Moriya interaction is reported for 5d-doped FeSi and CoSi, accompanied by a large modification of the spin stiffness and spiralization. Micromagnetic simulations coupled to atomistic spin-dynamics and ab initio magnetic interactions reveal the spin-spiral nature of the magnetic ground state and field-induced skyrmions for all these systems. Especially small skyrmions similar to 50 nm are predicted for Co0.75Os0.25Si, compared to similar to 148 nm for Fe0.75Co0.25Si. Convex-hull analysis suggests that all B20 compounds considered here are structurally stable at elevated temperatures and should be possible to synthesize. This prediction is confirmed experimentally by synthesis and structural analysis of the Ru-doped CoSi systems discussed here, both in powder and in single-crystal forms.

Despite extensive research on magnetic skyrmions and antiskyrmions, a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying, or even tailor-made, topologies. We address this challenge, by focusing on a construction pathway of skyrmionics metamaterial within a monolayer thin film and suggest several promising lattice-like, flakes-like, and cell-like skyrmionic metamaterials that are surprisingly stable. Central to our approach is the concept of 'simulated controlled assembly', in short, a protocol inspired by 'click chemistry' that allows for positioning topological magnetic structures where one likes, and then allowing for energy minimization to elucidate the stability. Utilizing high-throughput atomistic-spin-dynamic (ASD) simulations alongside state-of-the-art AI-driven tools, we have isolated skyrmions (topological charge Q=1), antiskyrmions (Q=-1), and skyrmionium (Q=0). These entities serve as foundational 'skyrmionic building blocks' to forming reported intricate textures. In this work, two key contributions are introduced to the field of skyrmionic systems. First, we present a novel method for integrating control assembly protocols for the stabilization and investigation of topological magnets, which marks a significant advancement in the ability to explore new skyrmionic textures. Second, we report on the discovery of skyrmionic metamaterials, which shows a plethora of complex topologies that are possible to investigate theoretically and experimentally.

Multiscale magnetic simulations, including micromagnetic and atomistic spin dynamics simulations, are widely used in the study of complex magnetic systems over a wide range of spatial and temporal scales. The advances in these simulation technologies have generated considerable amounts of data. However, a versatile and general tool for visualization, filtering, and denoising this data is largely lacking. To overcome these limitations, we have developed SpinView, a general interactive visual analysis tool for graphical exploration and data distillation. Combined with dynamic filters and a built-in database, it is possible to generate reproducible publication-quality images, videos, or portable interactive webpages within seconds. Since the basic input to SpinView is a vector field, it can be directly integrated with any spin dynamics simulation tool. With minimal effort on the part of the user, SpinView delivers a simplified workflow, speeds up analysis of complex datasets and trajectories, and enables new types of analysis and insight.

Stable magnetic states, including particle-like topological protected magnetic textures like skyrmions and hopfions, have recently shown a great contribution in various fields of science. In order to identify these textures in unknown magnetic systems, we have developed a hybrid neuroevolutionary algorithm based on shallow neural network to find stable magnetic states in Heisenberg spin model. Our algorithm combines global and local searching ability of both genetic algorithm (GA) and gradient descent(GD), and has no need of prior knowledge. It has capability of escaping from the local minimum in the potential energy surface (PES) by treating the topologically protected states as chromosomes and making genetic tunneling. It then navigates the spin configuration of the ground state of the magnetic Hamiltonian. We achieve great improvements in robustness and effectiveness compared to the traditional approaches like heat bath simulated annealing at zero Kelvin. These features make this algorithm as a promising tool for solving global optimization problems in magnetic systems.