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Repo of GAME-Net-UQ, a graph neural network with uncertainty quantification for predicting the DFT energy of adsorbed intermediates and transition states on monometallic surfaces.

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# GAME-Net-UQ

This repository contains the Python code used to train and evaluate GAME-Net-UQ, a graph neural network with uncertainty quantification (UQ) for predicting the DFT energy of relaxed species and transition states adsorbed on metal surfaces.

## Install

```bash
pip install gamenet-uq
```

The main dependencies of the repo can be found in [pyproject.toml](./pyproject.toml)

## Dataset

The datasets used to develop GAME-Net-UQ can be found in the Zenodo repository [https://doi.org/10.5281/zenodo.17977395](https://doi.org/10.5281/zenodo.17977395).

1. The ASE database (212 MB) containing all relaxed DFT structures and transition states is located at `gamenetuq_results/ASE_database/all.db`. 
2. The final, clean graph dataset (102 MB) in PyG format is located at `gamenetuq_results/PyG_graph_database/all_scaled_energy_025_125_2_False_False_False_True_False`. The graph dataset can be recreated from the ASE database with the [gen_dataset.py](./scripts/gen_dataset.py) script.

## Model training and finetuning

To train the model, run the script [train_mve.py](./scripts/train_mve.py). The [input template ](./scripts/input.toml) file provides an explanation for each entry required in the training configuration file.

```bash
python train_mve.py -i input.toml -o output_dirname
```

## Pretrained model

The final pretrained model can be employed with CARE ([link](https://github.com/LopezGroup-ICIQ/care)).

## License

The code is released under the [MIT](./LICENSE) license.

## Reference

Morandi, S., Loveday, O., Renningholtz, T. *et al.* An end-to-end framework for reactivity in heterogeneous catalysis. *Nat. Chem. Eng.* (2026). [https://doi.org/10.1038/s44286-026-00361-8](https://doi.org/10.1038/s44286-026-00361-8)

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