Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks.

ABSTRACT: Spectroscopic methods─like nuclear magnetic resonance, mass spectrometry, X-ray diffraction, and UV/visible spectroscopies─applied to molecular ensembles have so far been the workhorse for molecular identification. Here, we propose a radically different chemical characterization approach, based on the ability of noncontact atomic force microscopy with metal tips functionalized with a CO molecule at the tip apex (referred as HR-AFM) to resolve the internal structure of individual molecules. Our work demonstrates that a stack of constant-height HR-AFM images carries enough chemical information for a complete identification (structure and composition) of quasiplanar organic molecules, and that this information can be retrieved using machine learning techniques that are able to disentangle the

SUBMITTER: Carracedo-Cosme J 

PROVIDER: S-EPMC10176476 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature