xtal2png 0.6.0

Encode and decode crystal structures via portable networks graphics (PNG) files.

⚠️ Manuscript and results using a generative model coming soon ⚠️

xtal2png

Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Google's Imagen.

The latest advances in machine learning are often in natural language such as with LSTMs and transformers or image processing such as with GANs, VAEs, and guided diffusion models. Encoding/decoding crystal structures via grayscale PNG images is akin to making/reading a QR code for crystal structures. This allows you, as a materials informatics practitioner, to get streamlined results for new state-of-the-art image-based machine learning models applied to crystal structure. Let's take Google's text-to-image diffusion model, Imagen (unofficial), which can also be used as an image-to-image model. Rather than dig into the code spending hours, days, or weeks modifying, debugging, and playing GitHub phone tag with the developers before you can (maybe) get preliminary results, xtal2png lets you get those results using the default instructions on the repository.

After getting preliminary results, you get to decide whether it's worth it to you to take on the higher-cost/higher-expertise task of modifying the codebase and using a more customized approach. Or, you can stick with the results of xtal2png. It's up to you!

Quick Start

Installation

conda env create -n xtal2png -c conda-forge xtal2png
conda activate xtal2png

Example

# a list of `pymatgen.core.structure.Structure` objects
from xtal2png.utils.data import example_structures
from xtal2png.core import XtalConverter

xc = XtalConverter()  # DFT surrogate relaxation via m3gnet by default
data = xc.xtal2png(example_structures, show=True, save=True)
relaxed_decoded_structures = xc.png2xtal(data, save=False)


xc = XtalConverter(relax_on_decode=False)
data = xc.xtal2png(example_structures, show=True, save=True)
decoded_structures = xc.png2xtal(data, save=False)

Output

print(example_structures[0], decoded_structures[0], relaxed_decoded_structures[0])

Original	Decoded	Relaxed Decoded
Structure Summary Lattice abc : 5.033788 11.523021 10.74117 angles : 90.0 90.0 90.0 volume : 623.0356027127609 A : 5.033788 0.0 3.0823061808931787e-16 B : 1.8530431062799525e-15 11.523021 7.055815392078867e-16 C : 0.0 0.0 10.74117 PeriodicSite: Zn2+ (0.9120, 5.7699, 9.1255) [0.1812, 0.5007, 0.8496] PeriodicSite: Zn2+ (4.1218, 5.7531, 1.6156) [0.8188, 0.4993, 0.1504] ...	Structure Summary Lattice abc : 5.0250980392156865 11.533333333333331 10.8 angles : 90.0 90.0 90.0 volume : 625.9262117647058 A : 5.0250980392156865 0.0 0.0 B : 0.0 11.533333333333331 0.0 C : 0.0 0.0 10.8 PeriodicSite: Zn (0.9016, 5.7780, 3.8012) [0.1794, 0.5010, 0.3520] PeriodicSite: Zn (4.1235, 5.7554, 6.9988) [0.8206, 0.4990, 0.6480] ...	Structure Summary Lattice abc : 5.026834307381214 11.578854613685237 10.724087971087924 angles : 90.0 90.0 90.0 volume : 624.1953646135236 A : 5.026834307381214 0.0 0.0 B : 0.0 11.578854613685237 0.0 C : 0.0 0.0 10.724087971087924 PeriodicSite: Zn (0.9050, 5.7978, 3.7547) [0.1800, 0.5007, 0.3501] PeriodicSite: Zn (4.1218, 5.7810, 6.9693) [0.8200, 0.4993, 0.6499] ...

The before and after structures match within an expected tolerance; note the round-off error due to encoding numerical data as RGB images which has a coarse resolution of approximately 1/255 = 0.00392. Note also that the decoded version lacks charge states. The QR-code-like intermediate PNG image is also provided in original size and a scaled version for a better viewing experience:

64x64 pixels	Scaled for Better Viewing (tool credit)	Legend

Installation

Anaconda (conda) installation (recommended)

(2022-05-23, conda-forge installation still pending, fallback to pip install xtal2png as separate command)

Create and activate a new conda environment named xtal2png (-n) that will search for and install the xtal2png package from the conda-forge Anaconda channel (-c).

conda env create -n xtal2png -c conda-forge xtal2png
conda activate xtal2png

Alternatively, in an already activated environment:

conda install -c conda-forge xtal2png

If you run into conflicts with packages you are integrating with xtal2png, please try installing all packages in a single line of code (or two if mixing conda and pip packages in the same environment) and/or installing with mamba (source).

PyPI (pip) installation

Create and activate a new conda environment named xtal2png (-n) with python==3.9.* or your preferred Python version, then install xtal2png via pip.

conda env create -n xtal2png python==3.9.*
conda activate xtal2png
pip install xtal2png

Editable installation

In order to set up the necessary environment:

1. clone and enter the repository via:

   git clone https://github.com/sparks-baird/xtal2png.git
   cd xtal2png
   

2. create and activate a new conda environment (optional, but recommended)

   conda env create --name xtal2png python==3.9.*
   conda activate xtal2png
   

3. perform an editable (-e) installation in the current directory (.):

   pip install -e .
   

NOTE: Some changes, e.g. in setup.cfg, might require you to run pip install -e . again.

Optional and needed only once after git clone:

3. install several pre-commit git hooks with:

   pre-commit install
   # You might also want to run `pre-commit autoupdate`
   

   and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

4. install nbstripout git hooks to remove the output cells of committed notebooks with:

   nbstripout --install --attributes notebooks/.gitattributes
   

   This is useful to avoid large diffs due to plots in your notebooks. A simple nbstripout --uninstall will revert these changes.

Then take a look into the scripts and notebooks folders.

Command Line Interface (CLI)

Make sure to install the package first per the installation instructions above. Here is how to access the help for the CLI and a few examples to get you started.

Help

You can see the usage information of the xtal2png CLI script via:

(xtal2png) PS C:\Users\sterg\Documents\GitHub\sparks-baird\xtal2png> xtal2png --help

usage: xtal2png [-h] [--version] [-p STRING] [-s STRING] [--encode] [--decode] [-v] [-vv]

Crystal to PNG encoder/decoder.

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -p STRING, --path STRING
                        Crystallographic information file (CIF) filepath
                        (extension must be .cif or .CIF) or path to directory
                        containing .cif files or processed PNG filepath or path
                        to directory containing processed .png files (extension
                        must be .png or .PNG). Assumes CIFs if --encode flag is
                        used. Assumes PNGs if --decode flag is used.
  -s STRING, --save-dir STRING
                        Directory to save processed PNG files or decoded CIFs to.
  --encode              Encode CIF files as PNG images.
  --decode              Decode PNG images as CIF files.
  -v, --verbose         set loglevel to INFO
  -vv, --very-verbose   set loglevel to DEBUG

Examples

To encode a single CIF file located at src/xtal2png/utils/Zn2B2PbO6.cif as a PNG and save the PNG to the tmp directory:

xtal2png --encode --path src/xtal2png/utils/Zn2B2PbO6.cif --save-dir tmp

To encode all CIF files contained in the src/xtal2png/utils directory as a PNG and save corresponding PNGs to the tmp directory:

xtal2png --encode --path src/xtal2png/utils --save-dir tmp

To decode a single structure-encoded PNG file located at data/preprocessed/Zn8B8Pb4O24,volume=623,uid=b62a.png as a CIF file and save the CIF file to the tmp directory:

xtal2png --decode --path data/preprocessed/Zn8B8Pb4O24,volume=623,uid=b62a.png --save-dir tmp

To decode all structure-encoded PNG file contained in the data/preprocessed directory as CIFs and save the CIFs to the tmp directory:

xtal2png --decode --path data/preprocessed --save-dir tmp

Note that the save directory (e.g. tmp) including any parents (e.g. ab/cd/tmp) will be created automatically if the directory does not already exist.

Project Organization

├── AUTHORS.md              <- List of developers and maintainers.
├── CHANGELOG.md            <- Changelog to keep track of new features and fixes.
├── CONTRIBUTING.md         <- Guidelines for contributing to this project.
├── Dockerfile              <- Build a docker container with `docker build .`.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── preprocessed        <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yml         <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml          <- Build configuration. Don't change! Use `pip install -e .`
│                              to install for development or to build `tox -e build`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- [DEPRECATED] Use `python setup.py develop` to install for
│                              development or `python setup.py bdist_wheel` to build.
├── src
│   └── xtal2png            <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `pytest`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

Note on PyScaffold

This project has been set up using PyScaffold 4.2.1 and the dsproject extension 0.7.1.

To create the same starting point for this repository, as of 2022-06-01 on Windows you will need the development versions of PyScaffold and extensions, however this will not be necessary once certain bugfixes have been introduced in the next stable releases:

pip install git+https://github.com/pyscaffold/pyscaffold.git git+https://github.com/pyscaffold/pyscaffoldext-dsproject.git git+https://github.com/pyscaffold/pyscaffoldext-markdown.git

The following pyscaffold command creates a starting point for this repository:

putup xtal2png --github-actions --markdown --dsproj

Alternatively, you can edit a file interactively and update and uncomment relevant lines, which saves some of the additional setup:

putup --interactive xtal2png

Attributions

• @michaeldalverson for iterating through various representations during extensive work with crystal GANs. The base representation for xtal2png (see #output) closely follows a recent iteration (2022-06-13), taking the first layer ($1\times64\times64$) of the $4\times64\times64$ representation and replacing a buffer column/row of zeros with unit cell volume.