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QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy.

Jaime Carracedo-Cosme1,2, Carlos Romero-Muñiz3, Pablo Pou2,4

  • 1Quasar Science Resources S.L., Camino de las Ceudas 2, E-28232 Las Rozas de Madrid, Spain.

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Summary

The QUAM-AFM dataset offers 165 million simulated atomic force microscopy (AFM) images for 685,513 organic molecules, aiding chemical identification using deep learning.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Data Science

Background:

  • Atomic Force Microscopy (AFM) is crucial for molecular imaging.
  • Accurate chemical identification from AFM images remains a challenge.
  • Large-scale, diverse datasets are needed to train advanced analytical models.

Purpose of the Study:

  • Introduce the Quasar Science Resources-Autonomous University of Madrid atomic force microscopy image data set (QUAM-AFM).
  • Provide a comprehensive resource for advancing chemical identification in AFM experiments.
  • Facilitate the application of deep learning techniques to AFM data analysis.

Main Methods:

  • Generation of 165 million simulated AFM images from 685,513 organic molecules.
  • Creation of 3D image stacks with varying operational parameters and tip-sample distances.
  • Development of a graphical user interface for data retrieval and searching.

Main Results:

  • The QUAM-AFM dataset is the largest collection of simulated AFM images to date.
  • Each molecule is represented by 24 3D image stacks at 256x256 resolution.
  • Associated data includes chemical structures, IUPAC names, and atomic coordinates.

Conclusions:

  • The QUAM-AFM dataset is a valuable resource for deep learning-based chemical identification in AFM.
  • The dataset's scale and diversity support robust model training.
  • The provided interface enhances accessibility for researchers.