Machine learning for phase ordering dynamics of charge density waves

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.

16 pages, 9 figures

References in corpus (11)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• The Kernel Polynomial Method
• Big Data of Materials Science - Critical Role of the Descriptor
• Nearsightedness of Electronic Matter
• Self-Learning Monte Carlo Method
• Current-induced atomic dynamics, instabilities, and Raman signals: Quasi-classical Langevin equation approach
• Relaxation dynamics of the Holstein polaron
• Crossover in Growth Law and Violation of Superuniversality in the Random Field Ising Model
• Diagrammatic Monte Carlo method for many-polaron problems
• Coarsening in inhomogeneous systems
• Arrested phase separation in double-exchange models: machine-learning enabled large-scale simulation

Cited by in corpus (6)

• Ab initio electron-lattice downfolding: potential energy landscapes, anharmonicity, and molecular dynamics in charge density wave materials
• Learning by Confusion: The Phase Diagram of the Holstein Model
• Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
• Machine-learning force-field models for dynamical simulations of metallic magnets
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Machine learning approach for vibronically renormalized electronic band structures