Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations

• URL: http://arxiv.org/abs/2405.14776v1
• Date: Thu, 23 May 2024 16:44:29 GMT
• Title: Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
• Authors: Supriyo Ghosh, Sheng Zhang, Chen Cheng, Gia-Wei Chern,
• Abstract summary: We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of Jahn-Teller (JT) systems. Large scale dynamical simulations of the JT model shed light on the orbital ordering dynamics in colossal magnetoresistance manganites.
• Score: 7.540467064488348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems. Large scale dynamical simulations of the JT model also shed light on the orbital ordering dynamics in colossal magnetoresistance manganites. The JT effect in these materials describes the distortion of local oxygen octahedra driven by a coupling to the orbital degrees of freedom of $e_g$ electrons. An effective electron-mediated interaction between the local JT modes leads to a structural transition and the emergence of long-range orbital order at low temperatures. Assuming the principle of locality, a deep-learning neural-network model is developed to accurately and efficiently predict the electron-induced forces that drive the dynamical evolution of JT phonons. A group-theoretical method is utilized to develop a descriptor that incorporates the combined orbital and lattice symmetry into the ML model. Large-scale Langevin dynamics simulations, enabled by the ML force-field models, are performed to investigate the coarsening dynamics of the composite JT distortion and orbital order after a thermal quench. The late-stage coarsening of orbital domains exhibits pronounced freezing behaviors which are likely related to the unusual morphology of the domain structures. Our work highlights a promising avenue for multi-scale dynamical modeling of correlated electron systems.

Related papers

• Machine Learning Force-Field Approach for Itinerant Electron Magnets [3.3312479395168455]
  We review the recent development of machine-learning (ML) frameworks for Landau-Lifshitz-Gilbert (LLG) dynamics simulations. We show that LLG simulations based on local fields predicted by the trained ML models successfully reproduce representative non-collinear spin structures.
  arXiv  Detail & Related papers  (2025-01-10T18:50:45Z)
• Quantum many-body dynamics for fermionic t-J model simulated with atom arrays [1.8749305679160366]
  We propose a novel scheme to realize the fermionic t-J model in a programmable Rydberg-dressed tweezer array. We particularly explore quantum many-body dynamics in the large J/t limit, a regime far beyond the conventional optical lattices and cuprates. Our prediction opens a new horizon in exploring exotic quantum many-body dynamics with t-J model in tweezer arrays.
  arXiv  Detail & Related papers  (2024-12-31T17:28:40Z)
• Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations [4.94903410489486]
  phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics. We leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs) Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches.
  arXiv  Detail & Related papers  (2024-12-30T16:44:11Z)
• Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
  electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation. This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
  arXiv  Detail & Related papers  (2024-12-17T15:05:33Z)
• Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets [0.0]
  We present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of electron Ising systems. Our approach is reminiscent of the ML force-field models widely used in first-principles molecular dynamics simulations.
  arXiv  Detail & Related papers  (2024-11-29T15:35:37Z)
• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces novel deep dynamical models designed to represent continuous-time sequences. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Higher-order topological Peierls insulator in a two-dimensional atom-cavity system [58.720142291102135]
  We show how photon-mediated interactions give rise to a plaquette-ordered bond pattern in the atomic ground state. The pattern opens a non-trivial topological gap in 2D, resulting in a higher-order topological phase hosting corner states. Our work shows how atomic quantum simulators can be harnessed to investigate novel strongly-correlated topological phenomena.
  arXiv  Detail & Related papers  (2023-05-05T10:25:14Z)
• Machine learning for phase ordering dynamics of charge density waves [5.813015022439543]
  We present a machine learning framework for large-scale dynamical simulations of charge density wave (CDW) states. A neural-network model is developed to accurately and efficiently predict local electronic forces with input from neighborhood configurations. Our work highlights the promising potential of ML-based force-field models for dynamical simulations of functional electronic materials.
  arXiv  Detail & Related papers  (2023-03-06T21:00:56Z)
• Slow semiclassical dynamics of a two-dimensional Hubbard model in disorder-free potentials [77.34726150561087]
  We show that introduction of harmonic and spin-dependent linear potentials sufficiently validates fTWA for longer times. In particular, we focus on a finite two-dimensional system and show that at intermediate linear potential strength, the addition of a harmonic potential and spin dependence of the tilt, results in subdiffusive dynamics.
  arXiv  Detail & Related papers  (2022-10-03T16:51:25Z)
• Photoinduced prethermal order parameter dynamics in the two-dimensional large-$N$ Hubbard-Heisenberg model [77.34726150561087]
  We study the microscopic dynamics of competing ordered phases in a two-dimensional correlated electron model. We simulate the light-induced transition between two competing phases.
  arXiv  Detail & Related papers  (2022-05-13T13:13:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.