Machine learning force-field models for metallic spin glass
Menglin Shi Sheng Zhang Gia-Wei Chern
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.