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 November 21, 2024)
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Computational scanning tunneling microscope image database
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, , , Sergei Kalinin, Rama Vasudevan, Maxim Ziatdinov,
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
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Created December 5, 2021, Updated December 7, 2021