Post #782

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