Computational scanning tunneling microscope image database

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.

Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations

“…In addition, most electronic, magnetic, optical properties and their couplings, such as electrostatic moments, potentials and interaction energies, spin susceptibility, light absorption, and electromagnetic responses, etc, could be directly obtained starting from the ECD ρ ( r ) according to the modern band structure theory 2 . Because of these advantages, the basic quantity ρ ( r ) possesses broad applications, such as identifying the binding sites in host-guest compounds 5 , computing infrared intensities 6 , 7 , revealing structural stability, simulating scanning tunnelling microscopy images 8 and so on 9 – 11 . For instance, Shen et al .…”

“…This would be more accurate because the opt-B88vdW functional has been proved to give much improved lattice parameters for both van der Waals (vdW) and non-vdW solids 34 , which are essential to the calculations of accurate electronic charge density. In addition, such functional is usually adopted in the construction of many other properties related database 8 , 34 , 36 , 41 , hence our datasets would be useful for further analysis and comparisons since consistent computational procedures are adopted.…”

Recent Advances and Applications of Deep Learning Methods in Materials Science

“…DL has also been applied to classify symmetries in simulated STM measurements of 2D material systems [235]. DFT was used to generate simulated STM images for a variety of material systems.…”

“…In terms of images and spectra, the experimental data are too noisy most of the time and require much manipulation before applying DL, while theory based simulated data work, but being noise-free do not capture realistic scenarios [235].…”

Changes in polarization dictate necessary approximations for modeling electronic de-excitation intensity: an application to X-ray emission

“…Density Functional Theory (DFT) [1,2] has been the primary workhorse for electronic structure calculations over the last few decades [3][4][5][6], thanks to the accuracy it offers for modest computational expense. DFTbased first-principles calculations have not only helped in analyzing a plethora of experiments [7,8] performed on various materials, but have also contributed to efforts in the design and discovery of new functional materials [9,10]. Although Kohn-Sham (KS) DFT, in its pristine form, gives us access only to ground state properties, multiple methods, leveraging different levels of approximations, have been developed for accessing excited state properties starting from the ground-state information provided by DFT [11][12][13][14][15][16][17][18][19][20][21].…”