Machine Learning for Optical Scanning Probe Nanoscopy.
Xinzhong ChenSuheng XuSara ShabaniYueqi ZhaoMatthew FuAndrew J MillisMichael M FoglerAbhay N PasupathyMengkun LiuD N BasovPublished in: Advanced materials (Deerfield Beach, Fla.) (2022)
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.
Keyphrases
- high resolution
- artificial intelligence
- machine learning
- big data
- deep learning
- high speed
- mass spectrometry
- living cells
- quantum dots
- electron microscopy
- density functional theory
- molecular dynamics
- electronic health record
- single molecule
- drinking water
- fluorescent probe
- single cell
- energy transfer
- fluorescence imaging