Metallic spin glass systems, such as dilute magnetic alloys, are
characterized by randomly distributed local moments coupled to each other
through a long-range electron-mediated effective interaction. We present a
scalable machine learning (ML) framework for dynamical simulations of metallic
spin glasses. A Behler-Parrinello type neural-network model, based on the
principle of locality, is developed to accurately and efficiently predict
electron-induced local magnetic fields that drive the spin dynamics. A crucial
component of the ML model is a proper symmetry-invariant representation of
local magnetic environment which is direct input to the neural net. We develop
such a magnetic descriptor by incorporating the spin degrees of freedom into
the atom-centered symmetry function methods which are widely used in ML
force-field models for quantum molecular dynamics. We apply our approach to
study the relaxation dynamics of an amorphous generalization of the s-d model.
Our work highlights the promising potential of ML models for large-scale
dynamical modeling of itinerant magnets with quenched disorder.

金属自旋玻璃体系的动力学模拟中，利用可扩展的机器学习框架通过预测驱动自旋动力学的电子诱导的局部磁场，发展了一种根据局部磁性环境进行磁性描述的神经网络模型，该模型具有很高的精确性和高效性，并应用于具有淬灭随机性质的杂乱非晶常规 s-d 模型的弛豫动力学研究，展示了机器学习模型在大规模动力学建模中对游离磁体的有希望的潜力。