1. Machine Learning for Optical Scanning Probe Nanoscopy
- Author
-
Xinzhong Chen, Suheng Xu, Sara Shabani, Yueqi Zhao, Matthew Fu, Andrew J. Millis, Michael M. Fogler, Abhay N. Pasupathy, Mengkun Liu, and D. N. Basov
- Subjects
Condensed Matter - Materials Science ,Mechanics of Materials ,Physics - Data Analysis, Statistics and Probability ,Mechanical Engineering ,Materials Science (cond-mat.mtrl-sci) ,FOS: Physical sciences ,General Materials Science ,Data Analysis, Statistics and Probability (physics.data-an) ,Physics - Optics ,Optics (physics.optics) - Abstract
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. These tasks can be accomplished by the scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown 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. Augmented with AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.
- Published
- 2022