Extreme-Scale Atomistic Simulation of Real-Temperature Magnetic Skyrmion Dynamics by Coupled Spin-Lattice Modeling

2026-06-11

AI Summary

This work addresses the challenge of predictively simulating the formation and evolution of topological magnetic structures—such as magnetic skyrmions—in functional materials at realistic temperatures and device-relevant scales. The study proposes a coupled spin-lattice model that simultaneously captures atomic-scale lattice dynamics and spin evolution. For the first time, it enables predictive simulations of skyrmion nucleation and reconfiguration at trillion-atom scales under realistic thermal conditions, establishing a new paradigm for topological magnetodynamics. Leveraging key innovations—including a neural evolutionary potential trained on spin-constrained density functional theory, structure-preserving integrators, SVE2 vectorization, NUMA-aware data layout, and kernel fusion—the simulation achieves unprecedented scale and performance on the LineShine supercomputer, scaling to 12.45 million CPU cores with 89.7% weak-scaling efficiency, reaching 1.34 trillion atoms/spins and 48.5 PFLOPS in double-precision performance.

Abstract

Real-temperature topological magnetic dynamics in functional materials is governed by coupled lattice and spin evolution, yet remains inaccessible to predictive simulation at device-relevant scales. As a flagship example, thermally driven helix-to-skyrmion transformation in FeGe requires atomistic resolution, explicit lattice motion, and micrometer-scale domains to resolve device-scale topological texture formation. We combine a spin-constrained density-functional-theory-trained neuro-evolution potential with a structure-preserving spin-lattice integrator within one machine-learned framework. Architecture-specific optimizations, kernel fusion, SVE2 vectorization, and NUMA-aware data layout deliver a seven orders-of-magnitude speedup over prior spin-aware methods. Deployed on LineShine exascale supercomputer, the full application scales to 12.45 million CPU cores with 89.7% weak-scaling efficiency, enabling simulations of 1.34 trillion atoms and an equal number of spins while reaching 48.5 PFLOPS in double precision. The simulations directly resolve real-temperature skyrmion nucleation and reorganization at previously inaccessible scales, establishing a new regime for predictive simulation of coupled spin-lattice topological magnetic dynamics.

Problem

• magnetic skyrmion
• spin-lattice coupling
• atomistic simulation
• topological magnetic dynamics
• real-temperature simulation

Innovation

• spin-lattice coupling
• magnetic skyrmion
• machine-learned potential
• exascale simulation
• atomistic dynamics