Post #827

GraphML News (Feb 24th) - Orbital Materials Round, GNNs at LinkedIn, MLX-graphs ⚛️ Orbital Materials (founded by ex-DeepMind researchers) raised $16M Series A led by Radical Ventures and Toyota Ventures. OM focuses on materials science and shed some light on LINUS - the in-house 3D foundation model for material design (apparently, an ML potential and a generative model) with the ambition to become the AlphaFold of materials science. GNNs = 💸 🏋️‍♀️ LinkedIn published some details of their GNN architecture and GNN-powered services in the KDD’24 paper LiGNN: Graph Neural Networks at LinkedIn. The main graph is heterogeneous, multi-relational, and contains about 100B nodes and few hundred billion edges (rather sparse). The core GNN model is GraphSAGE is trained on linked prediction with various tweaks like temporal neighborhood sampling (from latest to older), PPR-based node sampling, and node ID embeddings. A few engineering tricks like multi-processing shared memory and smart node grouping allowed to speed up training from 24h down to 3 hours. LiGNN boosts recommendations and ads CTR. The bottom line: GNNs = 💸 🍏 Apple presented MLX-graphs: the GNN library for the MLX framework specifically optimized for Apple Silicon. Since the CPU/GPU memory is shared on M1/M2/M3, you don’t have to worry about moving tensors around and at the same time you can enjoy massive GPU memory of latest M2/M3 chips (64 GB MBPs and MacMinis are still much cheaper than A100 80 GB). For starters, MLX-graphs includes GCN, GAT, GIN, GraphSAGE, and MPNN models and a few standard datasets. 🧬 The OpenFold consortium announced SoloSeq and OpenFold-Multimer, open source and open weights analogues of ESMFold and AlphaFold-Multimer, respectively. The OpenFold repo already showed some signs of new modules, and now there is a public release. 👨‍🏫 Steven L Brunton (U Washington) released a new lecture video series on Physics Informed ML covering AI 4 Science applications enabled by (mostly geometric) deep learning that respect physical symmetries and invariances of the modeled system. This includes, for example, modeling fluid dynamics, PDEs, turbulence, and optimal control. A nice entrypoint into scientific applications! Weekend reading: Proteus: pioneering protein structure generation for enhanced designability and efficiency by Chentong Wang feat. Longxing Cao from Westlake - finally, a new protein generation model that seems to beat RFDiffusion and Chroma! Universal Physics Transformers by Benedikt Alkin feat Johannes Brandstetter Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation by Lingxiao Zhao, Xueying Ding, and Leman Akoglu (all CMU)