Machine Vision Automated Chiral Molecule Detection and Classification in Molecular Imaging

Scanning probe microscopy (SPM) is recognized as an essential characterization tool in a broad range of applications, allowing for real-space atomic imaging of solid surfaces, nanomaterials, and molecular systems. Recently, the imaging of chiral molecular nanostructures via SPM has become a matter of increased scientific and technological interest due to their imminent use as functional platforms in a wide scope of applications, including nonlinear chiroptics, enantioselective catalysis, and enantiospecific sensing. Due to the time-consuming and error-prone image analysis process, a highly efficient analytic framework capable of identifying complex chiral patterns in SPM images is needed. Here, we adopted a state-of-the-art machine vision algorithm to develop a one-image-one-system deep learning framework for the analysis of SPM images. To demonstrate its accuracy and versatility, we employed it to determine the chirality of the molecules comprising two supramolecular self-assemblies with two distinct chiral organization patterns. Our framework accurately detected the position and labeled the chirality of each molecule. This framework underpins the tremendous potential of machine learning algorithms for the automated recognition of complex SPM image patterns in a wide range of research disciplines.