Computational scanning tunneling microscope image database. Choudhary, K., Garrity, K. F., Camp, C., Kalinin, S. V., Vasudevan, R., Ziatdinov, M., & Tavazza, F. Scientific Data, 8(1):57, December, 2021. ![Computational scanning tunneling microscope image database [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{choudhary_computational_2021,
title = {Computational scanning tunneling microscope image database},
volume = {8},
issn = {2052-4463},
url = {http://www.nature.com/articles/s41597-021-00824-y},
doi = {10.1038/s41597-021-00824-y},
abstract = {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.},
language = {en},
number = {1},
urldate = {2021-03-18},
journal = {Scientific Data},
author = {Choudhary, Kamal and Garrity, Kevin F. and Camp, Charles and Kalinin, Sergei V. and Vasudevan, Rama and Ziatdinov, Maxim and Tavazza, Francesca},
month = dec,
year = {2021},
pages = {57},
}
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