QUAM–AFM Lite is the scaled-down version of QUAM-AFM, the largest dataset of simulated Atomic Force Microscopy (AFM) images. This reduced version was generated from a selection of 1,755 molecules that span the most relevant bonding structures and chemical species in organic chemistry. Similar to the extended version, QUAM-AFM Lite contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances (in the relevant imaging range and spanning a variation of 1 Å (0.1 nanometers)) with one of the 24 different combination of AFM operational parameters, resulting in a total of 421,200 images with a resolution of 256x256 pixels.
The operational parameters include six different values for the cantilever oscillation amplitude (0.40, 0.60, 0.80, 1.00, 1.20, 1.40Å), 4 values of the elastic constant describing the tilting of the CO tip (0.40, 0.60, 0.80 and 1.00 N/m). The first parameter is freely chosen in the experiments in order to enhance different features of the image, while the last one reflects differences in the attachment of the CO molecule to the metal tip that are routinely observed and has been characterized in the experiments.
The data provided for each molecule includes, besides a set of AFM images, the ball–and–stick depiction, the IUPAC name, the chemical formula, the atomic coordinates, and the 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 (GUI) that allows the search for structures by CID number, IUPAC name or chemical formula.
This dataset arises as a product of the research carried out in collaboration between Quasar Science Resources S.L. (https://quasarsr.com) and the Scanning Probe Microscopy Theory & Nanomechanics Research Group (SPMTH) (http://www.uam.es/spmth) at the Universidad Autónoma de Madrid (UAM), funded by the Comunidad de Madrid under the Industrial Doctorate Programme 2017 (project reference IND2017/IND-7793).
The main goal of this dataset is to provide a simplified version of QUAM-AFM that allows to analyse the distribution of information and/or the graphical interface without the need for a full download. The extended version, QUAM-AFM, supports the development of deep learning methods for molecular identification through AFM imaging. Once this project has concluded, this dataset is made freely accessible in order to facilitate and to promote research in a range of fields including Atomic Force Microscopy, on-surface synthesis and deep learning applications.
