Machine learning dynamics of phase separation in correlated electron magnets

Abstract

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.

6 pages, 4 figures

References in corpus (9)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• The Kernel Polynomial Method
• Scalar spin chirality and quantum Hall effect on a triangular lattice
• Self-Learning Monte Carlo Method
• Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
• Testing the Monte Carlo - Mean Field approximation in the one-band Hubbard model
• Semiclassical dynamics of spin density waves
• Phase separation in doped Mott insulators