Machine learning dynamics of phase separation in correlated electron magnets
This work enables large-scale dynamical simulations for researchers studying correlated electron systems, such as in colossal magnetoresistance, though it is incremental as it applies an existing machine-learning method to a specific computational bottleneck.
The authors tackled the problem of expensive real-time simulations of electronic phase separation in correlated electron magnets by using neural networks to achieve linear-scaling exchange field computation, enabling large-scale dynamical simulations that quantitatively reproduce exact results.
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.