论文标题

光学扫描探针纳米镜检查的机器学习

Machine Learning for Optical Scanning Probe Nanoscopy

论文作者

Chen, Xinzhong, Xu, Suheng, Shabani, Sara, Zhao, Yueqi, Fu, Matthew, Millis, Andrew J., Fogler, Michael M., Pasupathy, Abhay N., Liu, Mengkun, Basov, D. N.

论文摘要

执行纳米尺度光学成像和光谱的能力是破译量子材料中低能效应的关键,以及行星和外星颗粒,催化物质和水性生物学样品的振动指纹。散射型扫描近场光学显微镜(S-SNOM)技术最近扩展到许多研究领域并启用了著名发现。从这个简短的角度来看,我们表明S-SNOM与扫描探针研究一般可以从人工智能(AI)和机器学习(ML)算法中受益。我们表明,借助AI-和ML增强的数据获取和分析,扫描探针光学纳米镜检查有望变得更加有效,准确和智能。

The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. The scattering-type scanning near-field optical microscopy (s-SNOM) technique has recently spread to many research fields and enabled notable discoveries. In this brief perspective, we show that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial intelligence (AI) and machine learning (ML) algorithms. We show that, with the help of AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

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