Detecting nematic order in STM/STS data with artificial intelligence

Jeremy B. Goetz, Yi Zhang, M. Lawler
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引用次数: 3

Abstract

Detecting the subtle yet phase defining features in Scanning Tunneling Microscopy and Spectroscopy data remains an important challenge in quantum materials. We meet the challenge of detecting nematic order from local density of states data with supervised machine learning and artificial neural networks for the difficult scenario without sharp features such as visible lattice Bragg peaks or Friedel oscillation signatures in the Fourier transform spectrum. We train the artificial neural networks to classify simulated data of isotropic and anisotropic two-dimensional metals in the presence of disorder. The supervised machine learning succeeds only with at least one hidden layer in the ANN architecture, demonstrating it is a higher level of complexity than nematic order detected from Bragg peaks which requires just two neurons. We apply the finalized ANN to experimental STM data on CaFe2As2, and it predicts nematic symmetry breaking with 99% confidence (probability 0.99), in agreement with previous analysis. Our results suggest ANNs could be a useful tool for the detection of nematic order in STM data and a variety of other forms of symmetry breaking.
用人工智能检测STM/STS数据中的向列序
在扫描隧道显微镜和光谱数据中检测细微的相位特征仍然是量子材料的一个重要挑战。我们利用监督机器学习和人工神经网络从状态数据的局部密度中检测向列阶,以应对在傅里叶变换谱中没有明显特征(如可见的晶格布拉格峰或弗里德尔振荡特征)的困难场景。我们训练了人工神经网络对各向同性和各向异性二维金属在无序状态下的模拟数据进行分类。监督机器学习仅在人工神经网络架构中至少有一个隐藏层才能成功,这表明它比从布拉格峰检测到的向列顺序具有更高的复杂性,后者只需要两个神经元。我们将最终确定的人工神经网络应用于CaFe2As2上的实验STM数据,它预测向列对称性破缺的置信度为99%(概率为0.99),与之前的分析一致。我们的研究结果表明,人工神经网络可以成为检测STM数据中的向列顺序和各种其他形式的对称性破缺的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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