aidsorb 2.0.0

Python package for deep learning on molecular point clouds.

AIdsorb is a Python package for deep learning on molecular point clouds.

This package aims to provide a simple, easy-to-use and reproduce interface for:

• 📥 Creating molecular point clouds

• 🤖 Training DL algorithms on molecular point clouds

⚙️ Installation

[!IMPORTANT] It is strongly recommended to perform the installation inside a virtual environment.

Assuming an activated virtual environment:

pip install aidsorb

🚀 Usage

[!NOTE] Refer to the 📚 Documentation for more information.

Here is a summary of what you can do from the command line:

1. Visualize a point cloud:

   aidsorb visualize path/to/structure_or_pcd  # Structure (.xyz, .cif, etc) or .npy
   

2. Create and prepare point clouds:

   aidsorb create path/to/structures path/to/pcd_data  # Create and store point clouds
   aidsorb prepare path/to/pcd_data  # Split point clouds to train, valdation and test
   

3. Train and test a model:

   aidsorb-lit fit --config=path/to/config.yaml
   aidsorb-lit test --config=path/to/config.yaml --ckpt_path=path/to/ckpt
   

💡 Questions and Contributing

Questions

If you have any questions about how to use AIdsorb, we encourage you to post them in the 💬 Discussions section of the repository.

[!NOTE] Please make sure to read the documentation carefully first before asking your question.

Contributing

We welcome contributions from the community! Please read our 🙌 Contributing Guidelines before submitting PRs or opening issues.

📑 Citing

• To cite the software, please refer to the citation file or click the citation button.
• To cite the paper, please use the following BibTeX entry:

Show BibTex entry

@article{Sarikas2024,
  title = {Gas adsorption meets geometric deep learning: points, set and match},
  volume = {14},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-024-76319-8},
  DOI = {10.1038/s41598-024-76319-8},
  number = {1},
  journal = {Scientific Reports},
  publisher = {Springer Science and Business Media LLC},
  author = {Sarikas,  Antonios P. and Gkagkas,  Konstantinos and Froudakis,  George E.},
  year = {2024},
  month = nov
}

⚖️ License

AIdosrb is released under the GNU General Public License v3.0 only.