Search Results for author: Johannes Gasteiger

Found 9 papers, 6 papers with code

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

no code implementations6 Apr 2022 Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C. Lawrence Zitnick, Abhishek Das

Based on this analysis, we identify a smaller dataset that correlates well with the full OC20 dataset, and propose the GemNet-OC model, which outperforms the previous state-of-the-art on OC20 by 16%, while reducing training time by a factor of 10.

Initial Structure to Relaxed Energy (IS2RE)

Directional Message Passing on Molecular Graphs via Synthetic Coordinates

no code implementations NeurIPS 2021 Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann

We furthermore set the state of the art on ZINC and coordinate-free QM9 by incorporating synthetic coordinates in the SMP and DimeNet++ models.

Molecular Property Prediction

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More

no code implementations14 Jul 2021 Johannes Gasteiger, Marten Lienen, Stephan Günnemann

The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations.

GemNet: Universal Directional Graph Neural Networks for Molecules

2 code implementations NeurIPS 2021 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.


Diffusion Improves Graph Learning

2 code implementations NeurIPS 2019 Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann

In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).

Graph Learning Node Classification

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

4 code implementations ICLR 2019 Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann

We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.

Classification General Classification +1

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