论文标题
相关电子磁铁中相位分离的机器学习动力学
Machine learning dynamics of phase separation in correlated electron magnets
论文作者
论文摘要
我们证明了机器学习启用了双交换系统中电子相分离的大规模动力学模拟。该模型，也称为铁磁围绕晶格模型，被认为与巨大的磁磁性现象有关。对从电子哈密顿量计算出的交换力的这种不均匀状态的真实空间模拟对于大型系统而言可能会非常昂贵。在这里，我们显示，可以使用由小晶格上的精确计算训练的数据集训练的神经网络可以实现线性缩放交换场计算。我们的Landau-Lifshitz动力学模拟基于机器学习电位，不仅可以很好地再现非平衡松弛过程，而且还可以与精确模拟定量一致的相关函数。我们的工作为使用机器学习模型对相关电子系统进行大规模动态模拟铺平了道路。
We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system. This model, also known as the ferromagnetic Kondo lattice model, is believed to be relevant for the colossal magnetoresistance phenomenon. Real-space simulations of such inhomogeneous states with exchange forces computed from the electron Hamiltonian can be prohibitively expensive for large systems. Here we show that linear-scaling exchange field computation can be achieved using neural networks trained by datasets from exact calculation on small lattices. Our Landau-Lifshitz dynamics simulations based on machine-learning potentials nicely reproduce not only the nonequilibrium relaxation process, but also correlation functions that agree quantitatively with exact simulations. Our work paves the way for large-scale dynamical simulations of correlated electron systems using machine-learning models.
