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.
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