We introduce the first 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 will be made available on the JARVIS-DFT website (https://www.ctcms.nist.gov/~knc6/JVASP.html). 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 (CNN) model to identify Bravais lattices 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{arxiv.1912.09027,
title = {Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy},
author = {Kamal Choudhary and Kevin F. Garrity and Charles Camp and Sergei V. Kalinin and Rama Vasudevan and Maxim Ziatdinov and Francesca Tavazza},
journal= {arXiv preprint arXiv:1912.09027},
year = {2019}
}