atomvision 2022.10.23

atomvision

Atomvision

Table of Contents

• Introduction
• Installation
• Examples
• Reference
• How to contribute
• Correspondence
• Funding support

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

Examples

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 imagessample_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

Reference

1. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

2. Computational scanning tunneling microscope image database

Please see detailed publications list here.

How to contribute

For detailed instructions, please see Contribution instructions

Correspondence

Please report bugs as Github issues (https://github.com/usnistgov/atomvision/issues) or email to [e-mail].

Funding support

NIST-MGI (https://www.nist.gov/mgi).

Code of conduct

Please see Code of conduct