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.

我们介绍了使用二维 (2D) 材料密度泛函理论 (DFT) 获得的扫描隧道显微镜 (STM) 图像的系统数据库，并使用 Tersoff-Hamann 方法计算。目前它包含 716 种可剥离二维材料的数据。讨论了二维材料的五种可能的布拉维晶格类型及其傅立叶变换的示例。本工作中生成的所有计算 STM 图像均可在 JARVIS-STM 网站 (https://jarvis.nist.gov/jarvisstm) 上获取。我们发现所选材料的计算 STM 图像和实验 STM 图像之间具有极好的定性一致性。作为该数据库的第一个示例应用，我们训练了一个卷积神经网络模型来从 STM 图像中识别布拉维晶格。我们相信该模型可以帮助高通量实验数据分析。这些计算 STM 图像可以直接帮助识别相、分析实验 STM 图像中的缺陷和晶格畸变，以及合并到自主实验工作流程中。