Effective Ruderman-Kittel-Kasuya-Yosida-like interaction in diluted double-exchange model: self-learning Monte Carlo approach

Abstract

We study the site-diluted double exchange (DE) model and its effective Ruderman-Kittel-Kasuya-Yosida-like interactions, where localized spins are randomly distributed, with the use of the Self-learning Monte Carlo (SLMC) method. The SLMC method is an accelerating technique for Markov chain Monte Carlo simulation using trainable effective models. We apply the SLMC method to the site-diluted DE model to explore the utility of the SLMC method for random systems. We check the acceptance ratios and investigate the properties of the effective models in the strong coupling regime. The effective two-body spin-spin interaction in the site-diluted DE model can describe the original DE model with a high acceptance ratio, which depends on temperatures and spin concentration. These results support a possibility that the SLMC method could obtain independent configurations in systems with a critical slowing down near a critical temperature or in random systems where a freezing problem occurs in lower temperatures.

arXiv comment9 pages, 9 figures

References in corpus (20)

• Exchange Monte Carlo Method and Application to Spin Glass Simulations
• RKKY in half-filled bipartite lattices: graphene as an example
• Self-Learning Monte Carlo Method
• Chirality driven anomalous Hall effect in weak coupling regime
• Hybrid Monte Carlo algorithm for the Double Exchange Model
• Efficient Langevin Simulation of Coupled Classical Fields and Fermions
• Self-Learning Monte Carlo Method in Fermion Systems
• Self-learning Monte Carlo with Deep Neural Networks
• Frustration-induced insulating chiral spin state in itinerant triangular-lattice magnets
• Order N Monte Carlo Algorithm for Fermion Systems Coupled with Fluctuating Adiabatical Fields
• Self-learning Monte Carlo method with Behler-Parrinello neural networks
• The Truncated Polynomial Expansion Monte Carlo Method for Fermion Systems Coupled to Classical Fields: A Model Independent Implementation
• Magnetic Order in the Double Exchange Model in Infinite Dimensions
• Green-Function-Based Monte Carlo Method for Classical Fields Coupled to Fermions
• Monte Carlo Algorithm for the Double Exchange Model Optimized for Parallel Computations
• Inhomogeneous Ferromagnetism and Unconventional Charge Dynamics in Disordered Double Exchange Magnets
• Strong Suppression of the Spin Hall Effect in the Spin Glass State
• Low-temperature magnetic properties of the Kondo lattice model in one dimension
• Exact Green's function approach to RKKY interactions
• Self-learning Monte-Carlo for non-abelian gauge theory with dynamical fermions

Cited by in corpus (4)

• A perspective on machine learning and data science for strongly correlated electron problems
• Generative models for sampling and phase transition indication in spin systems
• Self-learning Monte Carlo with equivariant Transformer
• Sample generation for the spin-fermion model using neural networks