Reinforcement learning for optimizing magnetic skyrmion creation.

The topologically stabilized quasi-particle skyrmion is one of the most significant spin structures. Its unique physical properties-such as stability, nanoscale size, and efficient manipulability-make it a promising candidate for applications in high-density data storage, low-power in-memory computing, and neuromorphic devices. Skyrmions are typically generated from ferromagnetic states using field-tuning or current-tuning methods, which involve applying magnetic fields with varying gradients and sequences or spin-current pulses with specific amplitudes and polarizations. However, the complexity of these applied field or current sequences during skyrmion generation often leads to numerous intermediate phases, making the process repetitive and heavily reliant on trial and error. To address this challenge, we propose a phase-control method based on reinforcement learning (RL) to optimize field control for skyrmion generation. The RL framework incorporates a carefully designed reward system, guided by physical insights, that considers the topological number and feature states while encouraging diverse field-tuning modes. Training results demonstrate that the network can progressively learn and optimize the field sequences required for skyrmion generation. Once trained, the network is capable of autonomously and reliably generating skyrmions, significantly reducing the need for manual intervention and trial-and-error adjustments. This approach has broader potential applications, including the generation of other spintronic structures such as chiral domain walls and magnetic vortices. It represents a valuable contribution to AI-driven spintronic simulations, bridging the gap between computational models and experimental implementations, and advancing the development of next-generation spintronic technologies.