用机器学习模型加速自旋磁体动力学模拟，突破传统计算瓶颈。
Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets
- 用卷积神经网络直接预测自旋翻转的能量变化，避免重复求解电子结构。
- 在方形晶格双交换模型中发现低温下铁磁畴异常粗化现象。
- 适合研究大尺度电子关联自旋系统，可扩展至其他离散变量系统。
我们提出一种可扩展的机器学习（ML）框架，用于大规模动力学蒙特卡洛（kMC）模拟巡游电子伊辛系统。由于此类巡游磁体中自旋间的有效相互作用由导电电子介导，局部自旋更新引起的能量变化需反复求解电子结构问题，对大系统而言计算成本极高。基于局域性假设，构建了一个卷积神经网络（CNN）模型，可根据有限邻域内的伊辛构型直接预测对应的有效局部场及能量变化。由于CNN核大小固定，该模型可直接拓展至大晶格的kMC模拟。该方法类似于第一性原理分子动力学中广泛使用的机器学习力场模型。将该框架应用于方形晶格双交换伊辛模型，揭示了低温下铁磁畴的异常粗化行为。本工作展示了机器学习方法在类似具有离散动态变量的巡游系统大规模建模中的潜力。
原文摘要 · Abstract (English)
We present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force-field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables.
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