[](https://www.python.org/downloads/) [](https://pytorch.org/get-started/locally/) [](https://figshare.com/articles/dataset/RetNet/24598845) [](https://www.nature.com/articles/s41598-023-50309-8)

💎 RetNet

A 3D ConvNet that takes energy voxels as input and ouputs gas adsorption properties.

This repository contains a PyTorch implementation of RetNet, which can be found on model.py module.

🚀 Training RetNet

The following example is used to train RetNet on the University of Ottawa database[^2] for predicting CO₂ uptake.

[!IMPORTANT] It is strongly recommended to run all the scripts inside a virtual environment.

Clone the repository

bash git clone https://github.com/adosar/retnet cd retnet

Dependencies

1. Create a virtual environment: bash python -m venv <venvir_name>
2. Activate it: bash source <venvir_name>/bin/activate
3. Install the dependencies: bash (<venvir_name>) pip install -r requirements.txt

Collect the data

The following directory structure is required prior to training: bash data/ ├── MOFs ├── all_MOFs_screening_data.csv ├── batch_train │ ├── clean_names.json │ └── clean_voxels.npy └── batch_val_test ├── clean_names.json └── clean_voxels.npy

To achieve that:

1. Get the inputs: bash wget -O- 'https://figshare.com/ndownloader/files/43220463' | tar -xzvf-

   [!WARNING]
   If you use any of this data in your research work, you should cite the original work[^1].

2. Get the labels: bash wget -O- 'https://archive.materialscloud.org/record/file?filename=screening_data.tar.gz&record_id=62' | tar -xzvf- -C data/MOFs

   [!WARNING]
   If you use any of this data in your research work, you should cite the original work[^2].

Train the model

Check the comments in training.py to customize the training phase on your needs. bash (<venvir_name>) python training.py * GPU training time: 40s per epoch on Nvidia GTX 1650 Super * CPU training time: 257s per epoch on Intel i5 8400

[!TIP] If you want to use a GPU but the VRAM is not enough: 1. Decrease the training batch size to a value smaller than 64. 2. Remove some Conv layers or decrease the number of Conv filters.

📰 Citing

If you use the RetNet architecture in your research work or any of the scripts of this repository, please consider citing:

@article{Sarikas2024,
  title = {Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks},
  volume = {14},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-023-50309-8},
  DOI = {10.1038/s41598-023-50309-8},
  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 = jan 
}

[^1]: Sarikas, A.P., Gkagkas, K. & Froudakis, G.E. Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks. Sci Rep 14, 2242 (2024). https://doi.org/10.1038/s41598-023-50309-8

[^2]: Boyd, P.G., Chidambaram, A., García-Díez, E. et al. Data-driven design of metal–organic frameworks for wet flue gas CO₂ capture. Nature 576, 253–256 (2019). https://doi.org/10.1038/s41586-019-1798-7