Graph neural networks for ignition quality prediction
This is the source code for the paper Graph Neural Networks for Prediction of Fuel Ignition Quality (https://doi.org/10.1021/acs.energyfuels.0c01533).
We provide the source code for the paper Fuel Ignition Delay Maps for Molecularly Controlled Combustion(https://doi.org/10.1021/acs.energyfuels.4c00662) under branch IDT_maps.
You can also run the code via our free web frontend available here: https://www.avt.rwth-aachen.de/gnn
We also provide a video about this work at: GNN for predicting fuel ignition quality - Youtube
Overall model structure
Graph convolution
Required packages
The code is built upon:
which need to be installed before using our code.
Please follow their installation instructions: PyTorch Geometric, k-GNN, RDKit.
Usage
This repository contains following folders:
- Data: training and test data sets
- dep: dependencies to other packages
- smiles_to_molecular_graphs: convert SMILES strings into molecular graphs
- src: source code
- trained_models: trained model ensemble for predicting DCN, MON, RON
- training_script: train individual singletask or multitask models or apply transfer learning, e.g., variation of hyperparameters or data sets
How to cite this work
Please cite our paper if you use this code:
This paper:
@article{Schweidtmann.2020,
author = {Schweidtmann, Artur M. and Rittig, Jan G. and K{\"o}nig, Andrea and Grohe, Martin and Mitsos, Alexander and Dahmen, Manuel},
title = {Graph Neural Networks for Prediction of Fuel Ignition Quality},
journal = {Energy {\&} Fuels},
pages = {11395--11407},
volume = {34},
number = {9},
issn = {0887-0624},
year = {2020},
doi = {10.1021/acs.energyfuels.0c01533},
}Please also refer to the corresponding packages, that we use, if appropiate:
Pytorch Geomteric:
@inproceedings{Fey/Lenssen/2019,
title={Fast Graph Representation Learning with {PyTorch Geometric}},
author={Fey, Matthias and Lenssen, Jan E.},
booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
year={2019},
}k-GNN:
@inproceedings{morritfey.19,
author = {C. Morris and M. Ritzert and M. Fey and W. Hamilton and J.E. Lenssen and G. Rattan and M. Grohe},
booktitle = {Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 27.01.-01.02.2019, Honolulu, Hawaii, United States},
publisher = {{AAAI} Press},
title = {Weisfeiler and Leman go neural: Higher-order graph neural networks},
volume = {4602-4609},
year = {2019},
url = {http://arxiv.org/pdf/1810.02244v3},
}RDKit:
@misc{rdkit,
author = {{Greg Landrum}},
title = {RDKit: Open-Source Cheminformatics},
url = {http://www.rdkit.org}
}