GNNPCSAFT Project
The project focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT.
Currently, the model takes into account the hard-chain, dispersive, and associative terms of PC-SAFT. Future work on polar and ionic terms is being studied.
Use cases of this package are demonstrated in Jupyter Notebooks:
compare.ipynb(Open in Colab): comparison of the performance of trained modelstraining.ipynb(Open in Colab): notebook for model trainingtuning.ipynb(Open in Colab): notebook for hyperparameter tuning
Model checkpoints can be found at Hugging Face.
Implementations with GNNPCSAFT:
Citation
@article{lima2026,
title={Estimating PC-SAFT Pure-Component Parameters by Using Graph Neural Networks: Application to Ionic Liquids and Deep Eutectic Solvents},
author={Wildson Bernardino de Brito Lima and Osvaldo Chiavone-Filho and Everaldo Silvino dos Santos},
journal={J. Chem. Eng. Data},
year={2026},
volume={71},
number={6},
pages={2533--2544},
doi={10.1021/acs.jced.6c00027}
}
1 maintainer
Metadata
1 maintainer
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