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arXiv 2303.03493cond-mat.str-elcond-mat.stat-mechcs.LG
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用于电荷密度波相序动力学的机器学习

Machine learning for phase ordering dynamics of charge density waves

University of Virginia(弗吉尼亚大学)

Chen Cheng, Sheng Zhang, Gia-Wei Chern

AI总结:

针对大规模电荷密度波动力学模拟中电子力计算成本高的问题,提出基于局域性的神经网络力预测模型,实现线性复杂度模拟,发现Holstein模型中偏离Allen-Cahn定律的反常畴生长。

摘要:

我们提出了一种机器学习(ML)框架,用于电荷密度波(CDW)态的大规模动力学模拟。CDW态中的电荷调制通常伴随相应的结构畸变,且CDW序的绝热演化由晶格畸变的动力学支配。然而,对于大型系统,计算电子对驱动力的贡献在计算上成本极高。基于电子系统的局域性原理,我们开发了一种神经网络模型,可借助邻域构型的输入,准确且高效地预测局域电子力。重要的是,该ML模型使得CDW动力学模拟的线性复杂度算法成为可能。作为演示,我们将该方法应用于研究Holstein模型(一种CDW序的典范系统)的相序动力学。我们的大规模模拟揭示了一种有趣的CDW畴生长现象,其与伊辛型序参量场相序所预期的Allen-Cahn定律存在显著偏离。这种反常畴生长可归因于该系统中畴壁的复杂结构。我们的工作凸显了基于ML的力场模型在功能电子材料动力学模拟方面的可观潜力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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