Machine learning dynamics of phase separation in correlated electron magnets

• URL: http://arxiv.org/abs/2006.04205v1
• Date: Sun, 7 Jun 2020 17:01:06 GMT
• Title: Machine learning dynamics of phase separation in correlated electron magnets
• Authors: Puhan Zhang, Preetha Saha, Gia-Wei Chern
• Abstract summary: We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system. Our work paves the way for large-scale dynamical simulations of correlated electron systems using machine-learning models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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