arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1802.08862cond-mat.str-elcond-mat.dis-nncond-mat.stat-mech
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通过机器学习对强相关电子系统中的表面探针图像进行分类

Classifying surface probe images in strongly correlated electronic systems via machine learning

L. Burzawa, Shuo Liu, E. W. Carlson

摘要:

扫描探针实验,如扫描隧道显微镜(STM)和原子力显微镜(AFM)在强相关的电子系统往往揭示复杂的斑图形成在多个长度尺度。通过研究这些图像中的普遍尺度,我们已经在几个不同的相关电子系统中表明,斑图形成是由接近无序驱动的临界点驱动的,揭示了这些材料中斑图形成的统一。作为新材料图像分类问题的另一种方法,在这里,我们报告了机器学习方法的第一次调查,以确定哪个底层物理模型正在驱动系统中的斑图形成。使用神经网络架构,我们能够实现97%的准确率分类配置图像从三个模型与伊辛对称性。这项研究还表明,机器学习可以捕捉物理系统的隐含普遍行为。这拓宽了我们对机器学习可以做什么的理解,我们期待机器学习和凝聚态物理之间在未来有更多的协同作用。

英文摘要:

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated electronic systems often reveal complex pattern formation on multiple length scales. By studying the universal scaling in these images, we have shown in several distinct correlated electronic systems that the pattern formation is driven by proximity to a disorder-driven critical point, revealing a unification of the pattern formation in these materials. As an alternative approach to this image classification problem of novel materials, here we report the first investigation of the machine learning method to determine which underlying physical model is driving pattern formation in a system. Using a neural network architecture, we are able to achieve 97% accuracy on classifying configuration images from three models with Ising symmetry. This investigation also demonstrates that machine learning can capture the implicit universal behavior of a physical system. This broadens our understanding of what machine learning can do, and we expect more synergy between machine learning and condensed matter physics in the future.

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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