机器学习加速关联电子系统动力学模拟，发现电荷密度波增强粗化现象。
Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
- 用机器学习构建线性扩展算法，同时处理准粒子与集体激发
- 在哈伯德-霍尔斯坦模型中发现电子关联增强电荷密度波粗化
- 适合研究强关联体系非平衡动力学的科研人员参考
关联电子系统中涌现序的相位序动力学是非平衡物理中的基础课题，但尚未被充分探索。准粒子与涌现序参量场之间的复杂相互作用可能引发超出标准理论的异常粗化动力学。然而，在关联电子的动力学模拟中，准确处理准粒子与集体自由度存在多尺度挑战。本文利用现代机器学习方法，实现了对电荷密度波（CDWs）粗化的线性扩展模拟算法，该现象是功能电子材料中一种基本对称性破缺相。我们在方格子哈伯德-霍尔斯坦模型上验证了该方法，并揭示了一种由电子-电子相互作用屏蔽局域势导致的电荷密度波粗化增强现象。研究为电子关联在非平衡动力学中的作用提供了新见解，也凸显了机器学习力场方法在推进关联电子系统多尺度动力学建模方面的潜力。
原文摘要 · Abstract (English)
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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