Non--Contact Atomic Force Microscopy with CO--functionalized metal tips (referred to as HR-AFM) provides access to the internal structure of individual molecules adsorbed on a surface with totally unprecedented resolution. Previous works have shown that deep learning (DL) models can retrieve the chemical and structural information encoded in a 3D stack of constant-height HR--AFM images, leading to molecular identification. In this work, we overcome their limitations by using a well-established description of the molecular structure in terms of topological fingerprints, the 1024--bit Extended Connectivity Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure and similarity searching. ECFPs provide local structural information of the molecule, each bit correlating with a particular substructure within the molecule. Our DL model is able to extract this optimized structural descriptor from the 3D HR--AFM stacks and use it, through virtual screening, to identify molecules from their predicted ECFP4 with a retrieval accuracy on theoretical images of 95.4\%. Furthermore, this approach, unlike previous DL models, assigns a confidence score, the Tanimoto similarity, to each of the candidate molecules, thus providing information on the reliability of the identification. By construction, the number of times a certain substructure is present in the molecule is lost during the hashing process, necessary to make them useful for machine learning applications. We show that it is possible to complement the fingerprint-based virtual screening with global information provided by another DL model that predicts from the same HR--AFM stacks the chemical formula, boosting the identification accuracy up to a 97.6\%. Finally, we perform a limited test with experimental images, obtaining promising results towards the application of this pipeline under real conditions

How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing? Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland; aprego@ippt.pan.pl Abstract--The impact of Artificial Intelligence (AI) is expanding rapidly, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously needs novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by delivering tools for atomic-precision surface mapping. Besides many advantages, it also has some drawbacks, eg. In this paper, we focus on the potential possibilities for supporting SPM-based measurements, putting emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms as well as quantum computing (QC). It turned out that AI can be helpful in the experimental processes automation in routine operations, the algorithmic search for good sample regions, and shed light on the structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AIbased algorithms and QC may have a huge potential to increase the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for the improvement of AI-QC-powered SPM. I. INTRODUCTION scanning near field optical microscopy (SNOM) are universal tools for materials' surface characterization. SPM enables to obtain a high-resolution 3D surface profile in a nondestructive measurement.

Despite its wide range of uses, there are some problems with traditional atomic force microscopy (AFM) techniques. A high level of skill set and human interference is required for this procedure and is very time-consuming. In this article, the use of artificial intelligence (AI) in AFM and its combined benefits has been described. Atomic Force Microscopy (AFM) is a potent technology that permits the imaging of practically any surface along with polymers, ceramics, composites, glass, and biological materials. It is a surface scanning technique that has sub-nanometer scale resolution.

This paper introduces Quasar Science Resources–Autonomous University of Madrid atomic force microscopy image data set (QUAM-AFM), the largest data set of simulated atomic force microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip–sample distances with a different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256 256 pixels. The 3D stacks are especially appropriate to tackle the goal of the chemical identification within AFM experiments by using deep learning techniques. The data provided for each molecule include, besides a set of AFM images, ball-and-stick depictions, IUPAC names, chemical formulas, atomic coordinates, and map of atom heights. In order to simplify the use of the collection as a source of information, we have developed a graphical user interface that allows the search for structures by CID number, IUPAC name, or chemical formula.