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arXiv · 1912.09027

Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy

Kamal Choudhary·Kevin F. Garrity·Charles Camp·Sergei V. Kalinin·Rama Vasudevan·Maxim Ziatdinov·Francesca Tavazza
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Abstract

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.

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Kamal Choudhary, Kevin F. Garrity, Charles Camp, Sergei V. Kalinin, Rama Vasudevan, Maxim Ziatdinov, Francesca Tavazza. 2019-12-19. Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy. https://arxiv.org/abs/1912.09027

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