Machine learning spin dynamics in the double-exchange systems

The double-exchange (DE) mechanism plays an important role in our understanding of the colossal magnetoresistance phenomenon. It describes itinerant electrons interacting with local magnetic moments through the Hund's rule coupling. Although extensive effort has been devoted to studying the equilibrium properties of the DE models, dynamical phenomena in these systems remain much less explored, partly due to the expensive computational cost of their microscopic simulations. For example, in Landau-Lifshitz dynamics (LLD) simulations of the DE systems, the electron tight-binding Hamiltonian has to be solved at every time-step in order to obtain the torque acting on the local spins. Here we propose a machine learning (ML) technique that can solve the dynamics of the DE model in linear time complexity. Our approach is similar to the ML-based force prediction in quantum molecular dynamics. In our method, a deep-learning neural network trained by dataset from small system simulations is used to directly predict the effective local exchange force. We will also present our ML-enabled large-scale LLD simulation of phase separation phenomena in DE systems.