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Reference implementation of the DimeNet model proposed in the paper:
Directional Message Passing for Molecular Graphs
by Johannes Gasteiger, Janek Groß, Stephan Günnemann
Published at ICLR 2020.
As well as DimeNet++, its significantly faster successor:
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
by Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, Stephan Günnemann
Published at the ML for Molecules workshop, NeurIPS 2020.
Note that the author's name has changed from Johannes Klicpera to Johannes Gasteiger.
If you are interested in energy and force predictions, we now recommend our new model:
GemNet.
This repository contains a notebook for training the model (train.ipynb
) and for generating predictions on the test set with a trained model (predict.ipynb
). It also contains a script for training the model on a cluster with Sacred and SEML (train_seml.py
). For faster experimentation we also offer two sets of pretrained models, which you can find in the pretrained
folder.
The new DimeNet++ model is both 8x faster and 10% more accurate, so we recommend using this model instead of the original.
There are some slight differences between this repository and the original (TF1) DimeNet model, such as slightly different training and initialization in TF2. This implementation uses orthogonal Glorot initialization in the output layer for the targets alpha, R2, U0, U, H, G, and Cv and zero initialization for Mu, HOMO, LUMO, and ZPVE. The paper only used zero initialization for the output layer.
The following table gives an overview of all MAEs:
The repository uses these packages:
numpy
scipy>=1.3
sympy>=1.5
tensorflow>=2.1
tensorflow_addons
tqdm
Unfortunately there are a few issues/bugs in the code (and paper) that we can't fix without retraining the models. So far, these are:
_optimizer
variable of the MovingAverage
optimizer. This is only relevant if you actually load checkpoints from disk and continue training (DimeNet and DimeNet++).Please contact j.gasteiger@in.tum.de if you have any questions.
Please cite our papers if you use the model or this code in your own work:
@inproceedings{gasteiger_dimenet_2020,
title = {Directional Message Passing for Molecular Graphs},
author = {Gasteiger, Johannes and Gro{\ss}, Janek and G{\"u}nnemann, Stephan},
booktitle={International Conference on Learning Representations (ICLR)},
year = {2020}
}
@inproceedings{gasteiger_dimenetpp_2020,
title = {Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules},
author = {Gasteiger, Johannes and Giri, Shankari and Margraf, Johannes T. and G{\"u}nnemann, Stephan},
booktitle={Machine Learning for Molecules Workshop, NeurIPS},
year = {2020} }
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