MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917274308313088 |
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| author | Nonaka, Andy Tang, Yingheng LePelch, Julian C. Kumar, Prabhat Zhang, Weiqun Munoz, Jorge A. Fernandez-Soria, Christian Diaz, Cesar Gardner, David J. Yao, Zhi Jackie |
| author_facet | Nonaka, Andy Tang, Yingheng LePelch, Julian C. Kumar, Prabhat Zhang, Weiqun Munoz, Jorge A. Fernandez-Soria, Christian Diaz, Cesar Gardner, David J. Yao, Zhi Jackie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12242 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics Nonaka, Andy Tang, Yingheng LePelch, Julian C. Kumar, Prabhat Zhang, Weiqun Munoz, Jorge A. Fernandez-Soria, Christian Diaz, Cesar Gardner, David J. Yao, Zhi Jackie Computational Engineering, Finance, and Science Other Condensed Matter 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. |
| title | MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics |
| topic | Computational Engineering, Finance, and Science Other Condensed Matter |
| url | https://arxiv.org/abs/2602.12242 |