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.

arXiv comment6 pages, 4 figures

References in corpus (27)

• Adam: A Method for Stochastic Optimization
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Complexity in Strongly Correlated Electronic Systems
• How to detect fluctuating order in the high-temperature superconductors
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• Electronic Liquid Crystal Phases of a Doped Mott Insulator
• The Kernel Polynomial Method
• Scalar spin chirality and quantum Hall effect on a triangular lattice
• Stripe phases in high-temperature superconductors
• Self-Learning Monte Carlo Method
• Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
• Hidden Multiple-spin Interactions as an Origin of Spin Scalar Chiral Order in Frustrated Kondo Lattice Models
• Phase separation and stripe formation in the 2D t-J model: a comparison of numerical results
• Possible chiral phase transition in two-dimensional solid ${}^3$He
• Continuously Differentiable Exponential Linear Units
• Efficient Langevin Simulation of Coupled Classical Fields and Fermions
• Itinerant half-metal spin-density-wave state on the hexagonal lattice
• Testing the Monte Carlo - Mean Field approximation in the one-band Hubbard model
• T=0 Phase Diagram of the Double-Exchange Model
• Semiclassical dynamics of spin density waves
• Gradient-based stochastic estimation of the density matrix
• Phase separation in doped Mott insulators
• Machine learning electron correlation in a disordered medium
• Dynamics of Phase Separated States in the Double Exchange Model