Machine learning dynamics of phase separation in correlated electron magnets

The double-exchange mechanism plays an important role in our understanding of the colossal magnetoresistance phenomenon. Although extensive effort has been devoted to studying the equilibrium properties of the double-exchange models, dynamical phenomena in these systems remain much less explored. 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 non-equilibrium relaxation process, but also correlation functions that agree quantitatively with exact simulations. Our work opens new avenues for using deep-learning models to simulate and understand large-scale dynamical phenomena of correlated lattice systems.