https://github.com/usnistgov/atomvision
Deep learning framework for atomistic image data
https://github.com/usnistgov/atomvision
Last synced: 2 months ago
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Deep learning framework for atomistic image data
- Host: GitHub
- URL: https://github.com/usnistgov/atomvision
- Owner: usnistgov
- License: other
- Archived: true
- Created: 2021-09-16T20:33:46.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2025-06-27T04:28:30.000Z (3 months ago)
- Last Synced: 2025-06-27T05:32:41.401Z (3 months ago)
- Language: Python
- Homepage: https://pubs.acs.org/doi/10.1021/acs.jcim.2c01533
- Size: 122 MB
- Stars: 35
- Watchers: 8
- Forks: 17
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- best-of-atomistic-machine-learning - GitHub - 50% open · ⏱️ 25.08.2025): (Visualization)
README
[](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/AtomVisionExample.ipynb)
[](https://codecov.io/gh/usnistgov/atomvision)
[](https://badge.fury.io/py/atomvision)
[](https://pepy.tech/project/atomvision)
# Atomvision
⚠️ This repository is no longer maintained.
For the latest updates and continued development, please visit: https://github.com/atomgptlab/atomvision
# Table of Contents
* [Introduction](#intro)
* [Installation](#install)
* [Examples](#example)
* [Reference](#reference)
* [How to contribute](#contrib)
* [Correspondence](#corres)
* [Funding support](#fund)
Introduction
-------------------------
Atomvision is a deep learning framework for atomistic image data.
Installation
-------------------------
First create a conda environment:
Install miniconda environment from https://conda.io/miniconda.html
Based on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'.
Now,
```
bash Miniconda3-latest-Linux-x86_64.sh (for linux)
bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)
```
Download 32/64 bit python 3.6 miniconda exe and install (for windows)
Now, let's make a conda environment, say "version", choose other name as you like::
```
conda create --name vision python=3.8
source activate vision
```
Now, let's install the package:
#### Method 1 (using setup.py):
```
git clone https://github.com/usnistgov/atomvision.git
cd atomvision
python setup.py develop
```
#### Method 2 (using pypi):
As an alternate method, AtomVision can also be installed using `pip` command as follows:
```
pip install atomvision
```
#### Generating STEM image with convolution approximation: graphene example
```
stem_conv.py --file_path atomvision/tests/POSCAR --output_path STEM.png
```
#### 2D-Bravais lattice classification example
This example shows how to classify 2D-lattice (5 Bravais classes) for 2D-materials STM/STEM images.
We will use images``sample_data`` folder. It was generated with ``generate_stem.py`` script. There are two folders ``train_folder``, ``test_folder`` with sub-folders ``0,1,2,3,4,...`` for individual classes and they contain images for these classes.
```
train_classifier_cnn.py --model densenet --train_folder atomvision/sample_data/test_folder --test_folder atomvision/sample_data/test_folder --epochs 5 --batch_size 16
```
#### Generating a t-SNE plot
```
train_tsne.py --data_dir atomvision/sample_data/test_folder
```
#### Generative Adversarial Network
```
train_gan.py --dataset_path atomvision/sample_data/test_folder/0 --epochs 2
```
#### Autoencoder
```
train_autoencoder.py --train_folder atomvision/sample_data/test_folder --test_folder atomvision/sample_data/test_folder --epochs 10
```
1) [AtomVision: A machine vision library for atomistic images](https://arxiv.org/abs/2212.02586)
2) [The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design](https://www.nature.com/articles/s41524-020-00440-1)
3) [Computational scanning tunneling microscope image database](https://www.nature.com/articles/s41597-021-00824-y)
Please see detailed publications list [here](https://jarvis-tools.readthedocs.io/en/master/publications.html).
How to contribute
-----------------
For detailed instructions, please see [Contribution instructions](https://github.com/usnistgov/jarvis/blob/master/Contribution.rst)
Correspondence
--------------------
Please report bugs as Github issues (https://github.com/usnistgov/atomvision/issues) or email to kamal.choudhary@nist.gov.
Funding support
--------------------
NIST-MGI (https://www.nist.gov/mgi).
Code of conduct
--------------------
Please see [Code of conduct](https://github.com/usnistgov/jarvis/blob/master/CODE_OF_CONDUCT.md)