论文标题
Atomvision：原子图像的机器视觉库
AtomVision: A machine vision library for atomistic images
论文作者
论文摘要
计算机视觉技术在材料设计应用中具有巨大的潜力。在这项工作中，我们引入了一个集成的通用原子库库，该库可用于生成，策展扫描隧道显微镜（STM）和扫描传输电子显微镜（STEM）数据集并应用机器学习技术。 To demonstrate the applicability of this library, we 1) generate and curate an atomistic image dataset of about 10000 materials, 2) develop and compare convolutional and graph neural network models to classify the Bravais lattices, 3) develop fully convolutional neural network using U-Net architecture to pixelwise classify atom vs background, 4) use generative adversarial network for super-resolution, 5) curate a natural language processing based image dataset使用开放式ARXIV数据集，6）将计算框架与实验显微镜工具集成在一起。 Atomvision库可从https://github.com/usnistgov/atomvision获得。
Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate, curate scanning tunneling microscopy (STM) and scanning transmission electron microscopy (STEM) datasets and apply machine learning techniques. To demonstrate the applicability of this library, we 1) generate and curate an atomistic image dataset of about 10000 materials, 2) develop and compare convolutional and graph neural network models to classify the Bravais lattices, 3) develop fully convolutional neural network using U-Net architecture to pixelwise classify atom vs background, 4) use generative adversarial network for super-resolution, 5) curate a natural language processing based image dataset using open-access arXiv dataset, and 6) integrate the computational framework with experimental microscopy tools. AtomVision library is available at https://github.com/usnistgov/atomvision.
