论文标题
机器学习的自旋晶格潜力，用于动态模拟有缺陷的磁铁
A Machine-Learned Spin-Lattice Potential for Dynamic Simulations of Defective Magnetic Iron
论文作者
论文摘要
开发了用于磁铁的机器学习的自旋晶格（MSLP），并应用于介观尺度缺陷。它是通过增强旋转晶格的哈密顿量，其神经网络术语对代表局部原子构型和磁性环境的组合的描述术语进行训练。它以各种磁态再现了BCC和FCC相的内聚能。它可以预测与密度功能理论（DFT）中点缺陷的形成能量和复杂的磁性结构，包括在缺陷核心附近的磁矩的逆转和淬灭。 Curie温度是通过自旋晶格动力学计算得出的，在高温下显示出良好的计算稳定性。电势用于研究较大的脱位环附近的磁波动。 MSLP使用DFT和分子动力学超越了当前的处理，并超过了仅处理接近完美晶体病例的其他自旋晶格电位。
A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron is developed and applied to mesoscopic scale defects. It is achieved by augmenting a spin-lattice Hamiltonian with a neural network term trained to descriptors representing a mix of local atomic configuration and magnetic environments. It reproduces the cohesive energy of BCC and FCC phases with various magnetic states. It predicts the formation energy and complex magnetic structure of point defects in quantitative agreement with density functional theory (DFT) including the reversal and quenching of magnetic moments near the core of defects. The Curie temperature is calculated through spin-lattice dynamics showing good computational stability at high temperature. The potential is applied to study magnetic fluctuations near sizable dislocation loops. The MSLP transcends current treatments using DFT and molecular dynamics, and surpasses other spin-lattice potentials that only treat near-perfect crystal cases.
