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Found 11 papers, 1 papers with code
Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach
While the chiral phase is described by a broken $Z_2$ Ising-type symmetry, we find that the characteristic size of chiral domains increases linearly with time, in stark contrast to the expected Allen-Cahn domain growth law for a non-conserved Ising order parameter field.
Machine learning force-field models for metallic spin glass
Metallic spin glass systems, such as dilute magnetic alloys, are characterized by randomly distributed local moments coupled to each other through a long-range electron-mediated effective interaction.
Machine learning for structure-property relationships: Scalability and limitations
Based on the locality assumption, ML model is developed for the prediction of intensive properties of a finite-size block.
Machine learning for phase ordering dynamics of charge density waves
We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states.
Machine learning predictions for local electronic properties of disordered correlated electron systems
We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems.
Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systems
A general theory of the descriptor for the classical fields is formulated, and two types of models are distinguished depending on the presence or absence of an internal symmetry for the classical field.
Machine learning nonequilibrium electron forces for adiabatic spin dynamics
We present a generalized potential theory of nonequilibrium torques for the Landau-Lifshitz equation.
Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations
With the aid of modern machine learning methods, we demonstrate the first-ever large-scale kinetic Monte Carlo simulations of the phase separation process for the Falicov-Kimball model, which is one of the canonical strongly correlated electron systems.
Arrested phase separation in double-exchange models: machine-learning enabled large-scale simulation
We present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model based on deep-learning neural-network potentials trained from small-size exact diagonalization solutions.
Machine learning dynamics of phase separation in correlated electron magnets
We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system.
Machine learning electron correlation in a disordered medium
Learning from data has led to a paradigm shift in computational materials science.
