This page provides additional material for our papers

Directional Message Passing for Molecular Graphs
by Johannes Gasteiger, Janek Groß, and Stephan Günnemann
Published at the International Conference on Learning Representations (ICLR) 2020 (spotlight)

and

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
by Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann
Published at the Machine Learning for Molecules Workshop at NeurIPS 2020

Note that the author's name has changed from Johannes Klicpera to Johannes Gasteiger.

Directional Message Passing for Molecular Graphs

by Johannes Gasteiger, Janek Groß, and Stephan Günnemann
Published at the International Conference on Learning Representations (ICLR) 2020 (spotlight)

Abstract

Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however, consider the spatial direction from one atom to another, despite directional information playing a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions and spherical harmonics to construct theoretically well-founded, orthogonal representations that achieve better performance than the currently prevalent Gaussian radial basis representations while using fewer than 1/4 of the parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 76% on MD17 and by 31% on QM9.

Links

[ Paper | GitHub | Presentation | ICLR Virtual conference ]

Cite

Please cite our paper if you use the model, experimental results, or our 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} }

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

by Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann
Published at the Machine Learning for Molecules Workshop at NeurIPS 2020

Abstract

Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In this paper we aim to extend this scope in three ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more accurate than the original DimeNet on the QM9 benchmark of equilibrium molecules. Second, we validate DimeNet++ on highly reactive molecules by developing the challenging COLL dataset, which contains distorted configurations of small molecules during collisions. Finally, we investigate ensembling and mean-variance estimation for uncertainty quantification with the goal of accelerating the exploration of the vast space of non-equilibrium structures. Our DimeNet++ implementation as well as the COLL dataset are available online.

Links

[ Paper | GitHub | COLL dataset ]

Cite

Please cite our paper if you use the model, experimental results, or our code in your own work:

@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} }