https://github.com/priba/nmp_qc

Our own implementation of Neural Message Passing for Computer Vision paper
https://github.com/priba/nmp_qc

Last synced: 3 months ago
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Our own implementation of Neural Message Passing for Computer Vision paper

• Host: GitHub
• URL: https://github.com/priba/nmp_qc
• Owner: priba
• License: mit
• Created: 2017-04-19T19:10:59.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2020-05-05T11:06:49.000Z (over 4 years ago)
• Last Synced: 2024-08-02T06:19:32.645Z (6 months ago)
• Language: Python
• Homepage:
• Size: 201 KB
• Stars: 333
• Watchers: 16
• Forks: 82
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-pytorch - Neural Message Passing
• awesome-pytorch - Neural Message Passing

README

        
# Neural Message Passing for Quantum Chemistry

Implementation of different models of Neural Networks on graphs as explained in the article proposed by Gilmer *et al.* [1].

## Installation

    $ pip install -r requirements.txt

    $ python main.py

    

## Installation of rdkit

Running any experiment using QM9 dataset needs installing the [rdkit](http://www.rdkit.org/) package, which can be done 

following the instructions available [here](http://www.rdkit.org/docs/Install.html)

## Data

The data used in this project can be downloaded [here](https://github.com/priba/nmp_qc/tree/master/data).

## Bibliography

- [1] Gilmer *et al.*, [Neural Message Passing for Quantum Chemistry](https://arxiv.org/pdf/1704.01212.pdf), arXiv, 2017.

- [2] Duvenaud *et al.*, [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://arxiv.org/abs/1606.09375), NIPS, 2015.

- [3] Li *et al.*, [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493), ICLR, 2016. 

- [4] Battaglia *et al.*, [Interaction Networks for Learning about Objects](https://arxiv.org/abs/1612.00222), NIPS, 2016.

- [5] Kipf *et al.*, [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), ICLR, 2017

- [6] Defferrard *et al.*, [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), NIPS, 2016. 

- [7] Kearnes *et al.*, [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://arxiv.org/abs/1603.00856), JCAMD, 2016. 

- [8] Bruna *et al.*, [Spectral Networks and Locally Connected Networks on Graphs](https://arxiv.org/abs/1312.6203), ICLR, 2014.

 

 ## Cite

 

```

@Article{Gilmer2017,

  author  = {Justin Gilmer and Samuel S. Schoenholz and Patrick F. Riley and Oriol Vinyals and George E. Dahl},

  title   = {Neural Message Passing for Quantum Chemistry},

  journal = {CoRR},

  year    = {2017}

}

```

## Authors

* Pau Riba (@priba) [Webpage](http://www.cvc.uab.es/people/priba/)

* Anjan Dutta (@AnjanDutta) [Webpage](https://sites.google.com/site/2adutta/)