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.

翻译：为全面研究自旋电子器件中的多物理场耦合问题，必须采用并行化与GPU加速技术以应对相关物理过程中固有的时空尺度差异。此外，利用前沿时间积分库及机器学习方法替代并加速高计算成本的常规流程，将成为提升建模能力的关键发展方向。基于美国百亿亿次计算项目的AMReX软件框架、SUNDIALS时间积分库及基于Python的机器学习工作流，我们开发了名为MagneX的开源微磁学建模工具。该工具整合了多种关键磁耦合机制，包括塞曼耦合、退磁耦合、晶体各向异性相互作用、交换耦合及Dzyaloshinskii-Moriya相互作用（DMI）耦合。我们展示了该代码的GPU性能与可扩展性，并通过mumag标准问题和广泛认可的DMI基准测试对MagneX功能进行了严格验证。此外，我们利用基于仿真数据训练的神经网络库替代计算成本高昂的退磁物理过程，展示了MagneX的数据驱动能力。这种能够探索完整物理相互作用的新方法，为深入理解并开发全集成自旋电子与电子系统提供了前景广阔的研究路径。