Choudhary Kamal, Garrity Kevin F, Camp Charles, Kalinin Sergei V, Vasudevan Rama, Ziatdinov Maxim, Tavazza Francesca
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Sci Data. 2021 Feb 11;8(1):57. doi: 10.1038/s41597-021-00824-y.
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.
我们介绍了利用密度泛函理论（DFT）通过特索夫-哈曼方法计算得到的二维（2D）材料扫描隧道显微镜（STM）图像的系统数据库。它目前包含716种可剥离二维材料的数据。讨论了二维材料五种可能的布拉菲晶格类型及其傅里叶变换的示例。这项工作中生成的所有计算STM图像都可在JARVIS-STM网站（https://jarvis.nist.gov/jarvisstm）上获取。我们发现所选材料的计算STM图像与实验STM图像之间存在出色的定性一致性。作为该数据库的首个示例应用，我们训练了一个卷积神经网络模型，以从STM图像中识别布拉菲晶格。我们相信该模型有助于高通量实验数据分析。这些计算STM图像可直接辅助识别相、分析实验STM图像中的缺陷和晶格畸变，以及纳入自主实验工作流程。
