Counting Molecules: Python based scheme for automated enumeration and categorization of molecules in scanning tunneling microscopy images
This tool addresses the need for efficient analysis of on-surface chemical reactions and molecular structures in materials science, though it is incremental as it automates existing manual tasks.
The researchers tackled the problem of manually analyzing molecules in scanning tunneling microscopy images by developing an open-source Python-based scheme that automates the enumeration and categorization of molecules in scanned probe images, achieving automated processing for medium-sized images (10×10 to 100×100 nm).
Scanning tunneling and atomic force microscopies (STM/nc-AFM) are rapidly progressing to offer unprecedented spatial resolution of a diverse array of chemical species. In particular, they are employed to characterize on-surface chemical reactions by directly examining precursors and products. Chiral effects and self-assembled structures can also be investigated. This open source, modular, python based scheme automates the categorization of a variety of molecules present in medium sized (10$\times$10 to 100$\times$100 nm) scanned probe images.