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We demonstrate machine-learning enabled large-scale dynamical simulations ofelectronic phase separation in double-exchange system. This model, also knownas the ferromagnetic Kondo lattice model, is believed to be relevant for thecolossal magnetoresistance phenomenon. Real-space simulations of suchinhomogeneous states with exchange forces computed from the electronHamiltonian can be prohibitively expensive for large systems. Here we show thatlinear-scaling exchange field computation can be achieved using neural networkstrained by datasets from exact calculation on small lattices. OurLandau-Lifshitz dynamics simulations based on machine-learning potentialsnicely reproduce not only the nonequilibrium relaxation process, but alsocorrelation functions that agree quantitatively with exact simulations. Ourwork paves the way for large-scale dynamical simulations of correlated electronsystems using machine-learning models.

Machine learning dynamics of phase separation in correlated electron magnets