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Machine learning dynamics of phase separation in correlated electron magnets

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arxiv 2006.04205 v1 pith:JPXXUKD2 submitted 2020-06-07 cond-mat.str-el cond-mat.dis-nncond-mat.mes-hallcs.LG

Puhan Zhang , Preetha Saha , Gia-Wei Chern This is my paper · ORCID
classification cond-mat.str-elcond-mat.dis-nncond-mat.mes-hallcs.LG
keywords simulationselectronmachine-learningcorrelateddynamicaldynamicsexactexchange
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations

    cond-mat.str-el 2024-12 conditional novelty 7.0 of 10

    In the adiabatic Hubbard-Holstein model, increasing Hubbard repulsion accelerates charge density wave domain growth, restoring Allen-Cahn t^(1/2) coarsening via screening of the Holstein coupling.

  2. Echo State network for coarsening dynamics of charge density waves

    cond-mat.stat-mech 2024-12 conditional novelty 6.0 of 10

    A symmetry-aware echo state network trained on small-system charge-density-wave dynamics reproduces coarsening statistics on larger lattices and yields a growth exponent α≈0.375.

  3. Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets

    cond-mat.stat-mech 2024-11 conditional novelty 5.0 of 10

    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.

  4. Machine Learning Force-Field Approach for Itinerant Electron Magnets

    cond-mat.str-el 2025-01 conditional novelty 4.0 of 10

    A machine-learning force field with symmetry-preserving descriptors reproduces non-collinear spin dynamics and reveals arrested skyrmion ordering in triangular-lattice s-d models.

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