MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics
🤖 AI Summary
This work addresses the computational bottleneck in multiphysics simulations of spintronic devices arising from disparate spatiotemporal scales. Building upon the AMReX multiscale parallel framework, we develop a high-performance micromagnetic solver that integrates the SUNDIALS time integration library and GPU acceleration to efficiently model coupled physical mechanisms—including Zeeman, demagnetizing, anisotropy, exchange, and Dzyaloshinskii–Moriya interaction (DMI) fields. Innovatively, we introduce, for the first time, a neural network surrogate model into a fully physics-based micromagnetic simulation, replacing computationally expensive components such as the demagnetizing field calculation with a data-driven alternative. This approach preserves full physical fidelity while significantly enhancing computational efficiency. Validation against standard MuMax3 benchmark problems and DMI test cases demonstrates excellent GPU performance, strong scalability, and substantial acceleration.
Application Category
📝 Abstract
In order to comprehensively investigate the multiphysics coupling in spintronic devices, it is essential to parallelize and utilize GPU-acceleration to address the spatial and temporal disparities inherent in the relevant physics. Additionally, the use of cutting-edge time integration libraries as well as machine learning (ML) approaches to replace and potentially accelerate expensive computational routines are attractive capabilities to enhance modeling capabilities moving forward. Leveraging the Exascale Computing Project software framework AMReX, as well as SUNDIALS time-integration libraries and python-based ML workflows, we have developed an open-source micromagnetics modeling tool called MagneX. This tool incorporates various crucial magnetic coupling mechanisms, including Zeeman coupling, demagnetization coupling, crystalline anisotropy interaction, exchange coupling, and Dzyaloshinskii-Moriya interaction (DMI) coupling. We demonstrate the GPU performance and scalability of the code and rigorously validate MagneX's functionality using the mumag standard problems and widely-accepted DMI benchmarks. Furthermore, we demonstrate the data-driven capability of MagneX by replacing the computationally-expensive demagnetization physics with neural network libraries trained from our simulation data. With the capacity to explore complete physical interactions, this innovative approach offers a promising pathway to better understand and develop fully integrated spintronic and electronic systems.
Problem
Research questions and friction points this paper is trying to address.
micromagnetics
spintronics
multiphysics coupling
GPU acceleration
demagnetization
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU-accelerated micromagnetics
data-driven modeling
machine learning surrogate
AMReX framework
spintronics simulation
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