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Computational scanning tunneling microscope image database.

Kamal Choudhary1, Kevin F Garrity2, Charles Camp2

  • 1Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA. kamal.choudhary@nist.gov.

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|February 12, 2021
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A new database of scanning tunneling microscope (STM) images for 716 2D materials, calculated using density functional theory (DFT), shows excellent agreement with experimental data. This resource aids in identifying material properties and can automate experimental workflows.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Surface Science

Background:

  • Scanning tunneling microscopy (STM) is crucial for characterizing 2D materials.
  • Accurate simulation of STM images is essential for material identification and analysis.
  • A comprehensive database of simulated STM images is lacking for 2D materials.

Purpose of the Study:

  • To create a systematic database of simulated STM images for exfoliable 2D materials.
  • To provide a resource for validating computational methods and aiding experimental analysis.
  • To develop machine learning models for automated analysis of STM data.

Main Methods:

  • Density functional theory (DFT) calculations were performed to simulate STM images.
  • The Tersoff-Hamann method was employed for STM image generation.
  • A convolutional neural network (CNN) was trained to classify Bravais lattices from STM images.

Main Results:

  • A database of 716 2D materials with calculated STM images was established.
  • Excellent qualitative agreement was found between simulated and experimental STM images.
  • A CNN model demonstrated the ability to identify Bravais lattices from STM images.

Conclusions:

  • The JARVIS-STM database provides a valuable resource for 2D materials research.
  • The simulated STM images can aid in phase identification, defect analysis, and lattice distortion studies.
  • The developed CNN model facilitates high-throughput analysis of experimental STM data and integration into autonomous workflows.