Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations

• URL: http://arxiv.org/abs/2412.21072v1
• Date: Mon, 30 Dec 2024 16:44:11 GMT
• Title: Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
• Authors: Yang Yang, Chen Cheng, Yunhao Fan, Gia-Wei Chern,
• Abstract summary: phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics.<n>We leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs)<n>Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches.
• Score: 4.94903410489486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard theories. However, accurate treatment of both quasiparticles and collective degrees of freedom is a multi-scale challenge in dynamical simulations of correlated electrons. Here we leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs), one of the fundamental symmetry breaking phases in functional electron materials. We demonstrate our approach on the square-lattice Hubbard-Holstein model and uncover an intriguing enhancement of CDW coarsening which is related to the screening of on-site potential by electron-electron interactions. Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches for advancing multi-scale dynamical modeling of correlated electron systems.

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