MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics
2026-02-12 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors created a new open-source tool called MagneX to study how different magnetic effects work together in spintronic devices. They made it run fast on GPUs by using specialized software and also explored using machine learning to speed up heavy calculations. They tested MagneX to make sure it works well and showed it can replace some slow parts with neural networks. This helps scientists better understand and design devices that use magnetism and electronics together.
spintronicsmicromagneticsGPU accelerationmachine learningtime integrationZeeman couplingDzyaloshinskii-Moriya interactiondemagnetizationexchange couplingcrystalline anisotropy
Authors
Andy Nonaka, Yingheng Tang, Julian C. LePelch, Prabhat Kumar, Weiqun Zhang, Jorge A. Munoz, Christian Fernandez-Soria, Cesar Diaz, David J. Gardner, Zhi Jackie Yao
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.