A CNN trained on exact diagonalization predicts local spin-flip energies and enables large-scale kinetic Monte Carlo simulations that reveal temperature-dependent domain growth in a double-exchange Ising magnet.
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Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets
A CNN trained on exact diagonalization predicts local spin-flip energies and enables large-scale kinetic Monte Carlo simulations that reveal temperature-dependent domain growth in a double-exchange Ising magnet.