Chirality Detection in Scanning Tunneling Microscopy Data Using Artificial Intelligence.

ABSTRACT: Enantiospecific effects play an uprising role in chemistry and technical applications. Chiral molecular networks formed by self-assembly processes at surfaces can be imaged by scanning probe microscopy (SPM). Low contrast and high noise in the topography map often interfere with the automatic image analysis using classical methods. The long SPM image acquisition times restrain Artificial Intelligence-based methods requiring large training sets, leaving only tedious manual work, inducing human-dependent errors and biased labeling. By generating realistic looking synthetic images, the acquisition of real datasets is avoided. Two state-of-the-art object detection architectures are trained to localize and classify chiral unit-cells in a regular molecular chiral network formed by self-assembly

SUBMITTER: Seifert TJ 

PROVIDER: S-EPMC11671859 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature