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Computational scanning tunneling microscope image database

Published

Author(s)

Kamal Choudhary, Kevin Garrity, Charles Camp, Sergei Kalinin, Rama Vasudevan, Maxim Ziatdinov, Francesca Tavazza

Abstract

We introduce the systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable 2D materials. Examples of the five possible Bravais lattice types for 2D materials and their Fourier-transforms are discussed. All the computational STM images generated in this work are made available on the JARVIS-STM website (https://jarvis.nist.gov/jarvisstm). We find excellent qualitative agreement between the computational and experimental STM images for selected materials. As a first example application of this database, we train a convolution neural network model to identify the Bravais lattice from the STM images. We believe the model can aid high-throughput experimental data analysis. These computational STM images can directly aid the identification of phases, analyzing defects and lattice-distortions in experimental STM images, as well as be incorporated in the autonomous experiment workflows.
Citation
Scientific Data

Citation

Choudhary, K. , Garrity, K. , Camp, C. , Kalinin, S. , Vasudevan, R. , Ziatdinov, M. and Tavazza, F. (2021), Computational scanning tunneling microscope image database, Scientific Data, [online], https://doi.org/10.1038/s41597-021-00824-y (Accessed May 23, 2022)
Created December 5, 2021, Updated December 7, 2021