Spin Dynamic

Spin dynamics research focuses on understanding the time evolution of magnetic moments in materials, aiming to predict and control their behavior. Current efforts heavily utilize machine learning, particularly neural networks (including equivariant and graph neural networks), to model complex spin-spin interactions and efficiently simulate large-scale systems, often incorporating advanced techniques like Bayesian optimal experimental design to optimize experimental data collection. These advancements enable more accurate predictions of magnetic properties and phenomena like skyrmion formation and domain wall motion, with implications for developing novel spintronic devices and furthering our understanding of fundamental magnetism.