Identification of Non-Fermi Liquid Physics in a Quantum Critical Metal via Quantum Loop Topography,Physical Review Letters

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Identification of Non-Fermi Liquid Physics in a Quantum Critical Metal via Quantum Loop Topography
Physical Review Letters ( IF 8.1 ) Pub Date : 2021-07-22 , DOI: 10.1103/physrevlett.127.046601
George Driskell ₁ , Samuel Lederer ₁ , Carsten Bauer ₂ , Simon Trebst ₂ , Eun-Ah Kim ₁

Affiliation

Non-Fermi liquid physics is ubiquitous in strongly correlated metals, manifesting itself in anomalous transport properties, such as a

T

-linear resistivity in experiments. However, its theoretical understanding in terms of microscopic models is lacking, despite decades of conceptual work and attempted numerical simulations. Here we demonstrate that a combination of sign-problem-free quantum Monte Carlo sampling and quantum loop topography, a physics-inspired machine-learning approach, can map out the emergence of non-Fermi liquid physics in the vicinity of a quantum critical point (QCP) with little prior knowledge. Using only three parameter points for training the underlying neural network, we are able to robustly identify a stable non-Fermi liquid regime tracing the fans of metallic QCPs at the onset of both spin-density wave and nematic order. In particular, we establish for the first time that a spin-density wave QCP commands a wide fan of non-Fermi liquid region that funnels into the quantum critical point. Our study thereby provides an important proof-of-principle example that new physics can be detected via unbiased machine-learning approaches.

中文翻译：

通过量子环拓扑识别量子临界金属中的非费米液体物理

非费米液体物理学在强相关金属中无处不在，表现为异常的输运特性，例如

吨

- 实验中的线性电阻率。然而，尽管进行了数十年的概念工作和尝试数值模拟，但仍缺乏对微观模型的理论理解。在这里，我们证明了无符号问题的量子蒙特卡罗采样和量子环路拓扑的组合，一种受物理学启发的机器学习方法，可以绘制出量子临界点附近非费米液体物理学的出现（ QCP）几乎没有先验知识。仅使用三个参数点来训练底层神经网络，我们能够稳健地识别稳定的非费米液体状态，在自旋密度波和向列顺序开始时追踪金属 QCP 的扇形。特别是，我们第一次确定自旋密度波 QCP 控制了非费米液体区域的广泛粉丝，该区域汇集到量子临界点。因此，我们的研究提供了一个重要的原理验证示例，即可以通过无偏见的机器学习方法检测新物理。

更新日期：2021-07-22

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