Graph ML News (July 15th) - NeurIPS workshops, M2Hub

🪛 NeurIPS’23 announced the list of accepted workshops! Graph learning is well-presented, you might want to have a look at:

- AI for Accelerated Materials Design (AI4Mat-2023)
- AI for Science: from Theory to Practice
- Machine Learning and the Physical Sciences
- Machine Learning in Structural Biology Workshop
- New Frontiers in Graph Learning (GLFrontiers)
- New Frontiers of AI for Drug Discovery and Development
- Symmetry and Geometry in Neural Representations
- Temporal Graph Learning Workshop

We will keep an eye on the submission deadlines, generally you might expect them to be somewhere around the NeurIPS accepted papers announcement.

💠 M2Hub is a fresh collection of datasets and models for materials discovery: 11 datasets spanning organic and inorganic molecules and crystals, 8 models including EGNN, Equiformer, DimeNet, and GemNet, and more experiments in the fresh preprint. Materials discovery is catching up with drug discovery!

📈 UniMol, a 3D framework for molecular representation learning, has updated the performance of UniMol+ showing strong performance on OGB PCQM4M v2 and OpenCatalyst (OC20 IS2RE). Looks like this year’s OGB Large Scale challenge and Open Catalyst challenge are going to have a heated competition, eg, having in mind a recently released EquiformerV2.

🔬Simone Scardapane (Sapienza) prepared a nice slide deck on Designing and Explaining GNNs - the second half of the deck is about current explainability methods, have a look if you work in this area.

Weekend reading:

M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery

An OOD Multi-Task Perspective for Link Prediction with New Relation Types and Nodes