























Authors:Pin Chen, Cheng-bing Chen, Hai Liu, Yuewen Huang, Kangyou Zhong, Hai-Jun Zhao, Liu-Liu Han, Guixin Guo, Jiang Li, Dan Huang, Ben Xu, Yutong Lu
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.
From: Pin Chen [view email]
[v1]
Fri, 12 Jun 2026 03:39:11 UTC (8,713 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。