Post #869

GraphML News (Oct 19th) - Orb-v2, OMat24, Stanford Graph Learning Seminar, new PhD positions 🔥 The competition in materials science heats up: ML potentials (models that estimate the potential energy of an atomistic system and often predict energy, forces, and stresses) are one of the main drivers in the field as they can significantly speed up expensive molecular dynamics (MD) calculations. Matbench Discovery is one of the main benchmarks for ML potentials. 🔮 During the week, Orbital Materials released the code and weights of Orb-v2, the next version of the non-equivariant MPNN (Orbital folks explicitly bet against equivariant GNNs) that outperforms mighty MatterSim from MSR with just 25M parameters. Besides, Orb-v2 offers increased stability during MD calculations. 📈 A few days later, FAIR chemistry released OMat24, a new large dataset with 100M+ structures for training ML potentials (much larger than existing datasets) that required 400M+ core hours to complete DFT calculations for (preprint). Together with OMat24, FAIR released EquiformerV2, equivariant transformer, pre-trained on this dataset and fine-tuned on MatBench discovery (using just 64 A100s - 🌚 an entry-level 🌚 of compute those days) and claimed SOTA on Matbench Discovery. Interestingly, Equiformer got a significant performance boost when trained with the denoising objective - similar to what Orb models are trained on. It is likely that the benchmark will be fully saturated next year. Meanwhile, Google DeepMind together with Japanese institutes released a paper on applying GNoME (the flagship tool for materials discovery introduced last year) to synthesizing cesium chlorides. 🎙️ The Stanford Graph Learning Workshop will take place on November 5th physically at Stanford with the online stream, expect some new announcements and releases! 🎓 Finally, the application season for PhD positions and internships is open: we’d highlight the call for fully-funded PhD positions from Viacheslav Borovitskiy at the University of Edinburgh on Geometric Learning and Uncertainty Quantification (Geometric Kernels is one of the most recent works). Application deadline: Dec 15th, start date: September 2025. Let us know if your lab is hiring this season and we’ll compile a larger list of open geometric learning positions! Weekend reading: PDFs of ICLR 2025 submissions are now visible - you can open and read everything from the list we prepared a few weeks ago.