Machine learning for phase ordering dynamics of charge density waves

• URL: http://arxiv.org/abs/2303.03493v1
• Date: Mon, 6 Mar 2023 21:00:56 GMT
• Title: Machine learning for phase ordering dynamics of charge density waves
• Authors: Chen Cheng, Sheng Zhang, Gia-Wei Chern
• Abstract summary: 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.
• Score: 5.813015022439543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states. The charge modulation in a CDW state is often accompanied by a concomitant structural distortion, and the adiabatic evolution of a CDW order is governed by the dynamics of the lattice distortion. Calculation of the electronic contribution to the driving forces, however, is computationally very expensive for large systems. Assuming the principle of locality for electron systems, a neural-network model is developed to accurately and efficiently predict local electronic forces with input from neighborhood configurations. Importantly, the ML model makes possible a linear complexity algorithm for dynamical simulations of CDWs. As a demonstration, we apply our approach to investigate the phase ordering dynamics of the Holstein model, a canonical system of CDW order. Our large-scale simulations uncover an intriguing growth of the CDW domains that deviates significantly from the expected Allen-Cahn law for phase ordering of Ising-type order parameter field. This anomalous domain-growth could be attributed to the complex structure of domain-walls in this system. Our work highlights the promising potential of ML-based force-field models for dynamical simulations of functional electronic materials.

Related papers

• 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)
• GauSim: Registering Elastic Objects into Digital World by Gaussian Simulator [55.02281855589641]
  GauSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics, modeling each kernel as a continuous piece of matter to account for realistic deformations without idealized assumptions. GauSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• 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)
• 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)
• Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations [7.540467064488348]
  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.
  arXiv  Detail & Related papers  (2024-05-23T16:44:29Z)
• Pattern formation in charge density wave states after a quantum quench [5.155523246793565]
  We study post-quench dynamics of charge-density-wave (CDW) order in the square-lattice $t$-$V$ model. Our large-scale simulations uncover complex pattern formations in the post-quench CDW states, especially in the strong quench regime.
  arXiv  Detail & Related papers  (2023-12-21T10:50:26Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Spreading of a local excitation in a Quantum Hierarchical Model [62.997667081978825]
  We study the dynamics of the quantum Dyson hierarchical model in its paramagnetic phase. An initial state made by a local excitation of the paramagnetic ground state is considered. A localization mechanism is found and the excitation remains close to its initial position at arbitrary times.
  arXiv  Detail & Related papers  (2022-07-14T10:05:20Z)
• Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
  We present a constructive algorithm for generating quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
  arXiv  Detail & Related papers  (2021-04-01T19:06:00Z)
• Enhancement of quantum correlations and geometric phase for a driven bipartite quantum system in a structured environment [77.34726150561087]
  We study the role of driving in an initial maximally entangled state evolving under a structured environment. This knowledge can aid the search for physical setups that best retain quantum properties under dissipative dynamics.
  arXiv  Detail & Related papers  (2021-03-18T21:11:37Z)
• Machine learning dynamics of phase separation in correlated electron magnets [0.0]
  We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system. Our work paves the way for large-scale dynamical simulations of correlated electron systems using machine-learning models.
  arXiv  Detail & Related papers  (2020-06-07T17:01:06Z)

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.