latticegen 0.0.5
A small package to create images of atomic lattices
Requires Python: >=3.7

# Moiré lattice generator
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TAdeJong/moire-lattice-generator/HEAD?urlpath=lab)
![build](https://github.com/TAdeJong/moire-lattice-generator/workflows/build/badge.svg)
[![PyPI](https://img.shields.io/pypi/v/latticegen)](https://pypi.org/project/latticegen/)
[![Documentation Status](https://readthedocs.org/projects/moire-lattice-generator/badge/?version=latest)](https://moire-lattice-generator.readthedocs.io/en/latest/?badge=latest)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5156831.svg)](https://doi.org/10.5281/zenodo.5156831)

![test](https://moire-lattice-generator.readthedocs.io/en/latest/_images/source_Transparency_3_0.png)

Easily generate renders of lattices, moiré lattices and even quasi-lattices in Python.

Documentation: https://moire-lattice-generator.readthedocs.io

Magic angle bilayer graphene was shown to be superconducting in 2018 [[1](https://doi.org/10.1038/nature26160)]. 
Despite the considerable hype concerning this discovery, little code exists to visualize the moiré pattern of two graphene layers.

To illustrate the work as done in our own paper ["Direct evidence for flat bands in twisted bilayer graphene from nano-ARPES"](https://www.nature.com/articles/s41567-020-01041-x) ([arXiv version here](https://arxiv.org/abs/2002.02289)), I created this repository.

This repository contains Python code to generate lattices with values reasonably like experimental (e.g. STM or TEM) results.
*If you are looking to generate more schematic like hexagonal lattice drawings, have a look at [@alexkaz2](https://github.com/alexkaz2)'s excellent [hexalattice](https://github.com/alexkaz2/hexalattice).*

- Trigonal, hexagonal, square lattices as well as quasi lattices can be created and combined.
- Linear distortions, such as a uniaxial strain along an arbitrary direction and rotations are supported. In addition, arbitrary deformations can be rendered (by passing a deformation tensor to the `shift` parameter).
- Edge dislocations can be added to the lattice as well.

A simple Python notebook to interactively generate visualizations of moire patterns of hexagonal lattices at different angles is included.

A high resolution resulting movie of varying twist angle can be found [here](https://www.youtube.com/watch?v=c4n1pMsDNaU).

Furthermore, the effect of uniaxial deformation along a single direction as described in e.g. ["Measuring local moiré lattice heterogeneity of twisted bilayer graphene
"](https://doi.org/10.1103/PhysRevResearch.3.013153) can be visualized.

Click the "Launch binder" button above to open an interactive notebook directly in your browser. (Note: performance in the mybinder environment is somewhat slow. Download and run the notebook on a local machine for better performance.)

![moire pattern](https://repository-images.githubusercontent.com/292806144/bd108280-7081-11eb-8e03-2018853e1909)

## Installation 

### Using pip

The package is available on pypi:

```bash
pip install latticegen
```


### From source
If you want to install from source, that is of course also possible:

```
git clone https://github.com/TAdeJong/moire-lattice-generator.git
cd moire-lattice-generator
pip install .
```

If you want to be able to play around with the functions themselves, consider using `pip install -e .`.

### Using conda:

Not yet available in conda-forge, but you can install it in a conda environment using pip. There is an `environment.yml` located in the binder folder in this project which can be used to create the environment:
`conda env create -f binder/environment.yml`

## Development

### Testing

This project uses `pytest` and `hypothesis` to run tests.

Install the test dependencies:

```
$ pip install -r requirements_test.txt
```
To run the tests:

```
$ pytest
```

### Releasing

Releases are published to PyPI by github actions when a tag is pushed to GitHub. (Note: we are using versioneer for version management)

# Acknowledgement

This work was financially supported by the [Netherlands Organisation for Scientific Research (NWO/OCW)](https://www.nwo.nl/en/science-enw) as part of the [Frontiers of Nanoscience (NanoFront)](https://www.universiteitleiden.nl/en/research/research-projects/science/frontiers-of-nanoscience-nanofront) program.