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 monometallic transition metal surfaces.
We will soon provide a .yml file from which generate the conda environment needed for the code. Main dependencies are: Python 3.11, Pytorch, Pytorch Geometric, ASE.
The DFT dataset fg.db (217 MB) used to train the GNN will be soon uploaded to Zenodo as ASE database including the DFT VASP relaxed geometries, simulation settings, and other metadata.
The graph dataset (92 MB) can be automatically generated from the ASE database with the script scripts/gen_dataset.py.
To train the model, run the script scripts/train_mve.py -i config.toml -o OUTDIR. The TEMPLATE.toml file provides an explanation for each entry required in the training configuration file.
The final pretrained model is available within CARE (link).
The code is released under the MIT license.
- A Foundational Model for Reaction Networks on Metal Surfaces
Authors: S. Morandi, O. Loveday, T. Renningholtz, S. Pablo-García, R. A. Vargas Hernáńdez, R. R. Seemakurthi, P. Sanz Berman, R. García-Muelas, A. Aspuru-Guzik, and N. López
DOI: 10.26434/chemrxiv-2024-bfv3d