AtomVision: A Machine Vision Library for Atomistic Images.
Kamal ChoudharyRamya GurunathanBrian L DeCostAdam J BiacchiPublished in: Journal of chemical information and modeling (2023)
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 and curate microscopy image (such as scanning tunneling microscopy and scanning transmission electron microscopy) data sets and apply a variety of machine learning techniques. To demonstrate the applicability of this library, we (1) establish an atomistic image data set of about 10 000 materials with large structural and chemical diversity, (2) develop and compare convolutional and atomistic line graph neural network models to classify the Bravais lattices, (3) demonstrate the application of fully convolutional neural networks using U-Net architecture to pixelwise classify atom versus background, (4) use a generative adversarial network for super resolution, (5) curate an image data set on the basis of natural language processing using an open-access arXiv data set, and (6) integrate the computational framework with experimental microscopy images for Rh, Fe 3 O 4 , and SnS systems. The AtomVision library is available at https://github.com/usnistgov/atomvision.
Keyphrases
- deep learning
- convolutional neural network
- electron microscopy
- machine learning
- neural network
- high resolution
- big data
- artificial intelligence
- electronic health record
- optical coherence tomography
- single molecule
- molecular dynamics simulations
- high speed
- high throughput
- label free
- climate change
- autism spectrum disorder
- single cell
- transition metal