Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks.

Recent release of open-source machine learning libraries presents opportunities to unify machine learning with nanoscale research, thus improving effectiveness of research methods and characterization protocols. This paper outlines and demonstrates the effectiveness of such a synergy with artificial neural networks to provide for an accelerated and enhanced characterization of individual carbon nanotubes deposited over a surface. Our algorithms provide a rapid diagnosis and analysis of tip-enhanced Raman spectroscopy mappings and the results show an improved spectral assignment of spectral features and spatial contrast of the collected images.