论文标题
计数分子：基于Python的方案，用于扫描隧道显微镜图像中分子自动枚举和分类
Counting Molecules: Python based scheme for automated enumeration and categorization of molecules in scanning tunneling microscopy images
论文作者
论文摘要
扫描隧道和原子力显微镜（STM/NC-AFM）正在迅速发展，以提供多种化学物种阵列的前所未有的空间分辨率。特别是，它们被用来通过直接检查前体和产品来表征地表化学反应。还可以研究手性效应和自组装结构。这种开源，模块化的基于Python的方案可自动化中等大小的各种分子的分类（10 $ \ times $ 10至100 $ \ times $ \ times $ 100 nm）扫描探针图像。
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.
