428 citations · 1.3k across the 8 of their papers we have counts for
8 papers
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann
Graph neural networks that leverage coordinates via directional message passing have recently set the state of the art on multiple molecular property prediction tasks. However, the…
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Johannes Gasteiger, Marten Lienen, Stephan Günnemann
The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations. This algorithm runs in quadratic time as it requires the full…
GemNet: Universal Directional Graph Neural Networks for Molecules
Johannes Gasteiger, Florian Becker, Stephan Günnemann
Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations. Graph…
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
Johannes Gasteiger, Shankari Giri, Johannes T. Margraf +1
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 mole…
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness.…
Directional Message Passing for Molecular Graphs
Johannes Gasteiger, Janek Groß, Stephan Günnemann
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 d…