Post #804

​​GraphML News (Nov 4th) - AlphaFold 2.3 for docking, OpenDAC, KwikBucks 🧬 Google DeepMind and Isomorphic Labs announced AlphaFold 2.3 (with a funny PDF name like Jupyter notebooks) - the newest iteration is crushing the baselines in 3 tasks: docking benchmarks (almost 2x better than DiffDock on PoseBusters), protein-nucleic acid interactions, and antibody-antigen prediction. Most of the paper’s content is devoted to experimental results and some examples. Following the trend of big AI labs, the document is authored by “teams” and has no details on the model architecture — from those 3 paragraphs in the model section, an educated guess might be an equivariant Transformer architecture. I would also add proper citations to the ML docking baselines, eg, DiffDock and TankBind, they deserved it 🙂 💎 Prepare your GemNets and EquiFormers: Open Direct Air Capture (OpenDAC) is a collab between Meta AI and Georgia Tech on discovering new sorbents for capturing CO2 from the air. OpenDAC is a massive dataset that includes 40M DFT calculations from 170K relaxations and will be a part of the OpenCatalyst (OCP) project. OCP delivers a lot of cool stuff this year - apart from OpenDAC they released AdsorbML for the NeurIPS’23 challenge. 💸 Google Research published a blog post on KwikBucks (the person who came up with the name deserves a peer bonus), a clustering method based on the graph representation. Mainly designed for text clustering and document retrieval, the algorithm also works on standard graphs like Cora and Amazon Photos. In case graphs have no features, the authors run Deep Graph Infomax (DGI) to get unsupervised features. A few shorter updates: The Workshop on AI-driven discovery for physics and astrophysics (AI4Phys) organized by Center for Data-Driven Discovery and Simons Foundations will take place on January 22-26th at the University of Tokyo. Meanwhile, the proceedings of the NeurIPS AI4Mat Workshop (AI for Accelerated Materials Design) are now available. LoG is approaching and so are the local meetups! The LoG Meetup at EPFL in Lausanne will be held on Nov 22nd and the meetup at TUM in Munich will be held on Nov30th-Dec 1st. Weekend reading: Scaling Riemannian Diffusion Models by Aaron Lou, Minkai Xu, and Stefano Ermon (Stanford) Equivariant Matrix Function Neural Networks by Ilyes Batatia feat. Gábor Csányi (Cambridge) A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning by Samuel E. Otto feat. Steven L. Brunton (UW) - a massive work on symmetries and equivariances in neural nets highlighting the effectiveness of Lie derivatives Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks by Erfan Loghmani and MohammadAmin Fazli (Sharif) - on the losses for temporal graph learning, also introduces the Myket dataset already integrated into PyG