GraphML News (Jan 6th) - ICLR’24 workshops, new blog posts, MACE-MP-0 potential

We are getting back with the weekly news, hope you had a nice winter holiday!

🎤 ICLR’24 started announcing accepted workshops, the list is (so far) incomplete, but we might expect some graph and geometric learning here:

- AI for Differential Equations in Science
- Generative and Experimental Perspectives for Biomolecular Design
- Machine Learning for Genomics Explorations

📝 New blogposts!

▶️ Pat Walters started a massive series on AI in Drug Discovery in 2023: part 1 covers benchmarks, deep learning for docking, and AlphaFold for ligand discovery and design. Part 2 will focus on LLMs and generative models, Part 3 will be on review articles.

▶️ Zhaocheng Zhu, Michael Galkin, Abulhair Saparov, Shibo Hao, and Yihong Chen review the landscape of LLM reasoning approaches covering tool usage, retrieval, planning, and open reasoning problems. Lots of unsolved theoretical and practical problems to work on in 2024!

⚛️ Ilyes Batatia and a huge collab from Cambrige, Oxford, and EU universities announced MACE-MP-0: a foundational ML potentials model that can accurately approximate DFT calculations needed for molecular dynamics and atomistic simulations. The model is based on MACE (equivariant MPNN) and was trained on the Materials Project to predict forces, energy, and stress on 150k crystal structures for 200 epochs on 40-80 A100’s (definitely not a GPU-poor project, perhaps GPU-middle class). The authors ran about 30 experiments studying a single pre-trained model with different crystal structures and atomistic systems. The race for ML potentials has officially started 🏎️

Weekend reading:

Learning Scalable Structural Representations for Link Prediction with Bloom Signatures by Zhang et al. feat Pan Li - hashing-based link prediction now with Bloom filters

Scalable network reconstruction in subquadratic time by Tiago Peixoto (Mr. GraphTool) - present a O(N log^2 N) algorithm for network reconstruction

A foundation model for atomistic materials chemistry by Batatia et al - MACE-MP-0