GraphML News (Nov 18th) - GraphCast and Chroma release, Neural Circulation Models

While one half of the world digests the drama around OpenAI and comes up with conspiracy theories and another half is working on ICLR rebuttals and CVPR deadlines, let’s look at the GraphML news!

☔ Two models we first spotted and mentioned in 2023 The State of Affairs post were officially released as Science and Nature publications: GraphCast from Google DeepMind for weather prediction and Chroma for protein design from Generate Biomedicines. Both GraphCast and Chroma are open-sourced on Github (GraphCast repo, Chroma repo), huge kudos for the authors for doing that 👏 

🏟️ Both Chroma and RFDiffusion will be the keynotes at the MLSB workshop at NeurIPS, and Gabriele Corso already suggests to prepare some 🍿 to see the final showdown of the two heavy-weight generative champions (with EvoDiff in the interlude).

Google Research and DeepMind went an extra mile and uploaded a new paper on Neural General Circulation Models that already outperforms GraphCast on several tasks. The core component of NeuralGCM is a differentiable ODE solver, but otherwise it’s the encode-process-decode architecture with MLPs.

Xiaoxin He compiled a list of graph papers to be presented at NeurIPS’23 - a handy tool to get ready for the poster sessions!

Weekend reading:

A new age in protein design empowered by deep learning by Hamed Khakzad et al feat Michael Bronstein and Bruno Correia - a survey on (geometric) DL models for protein design including hot generative models.

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges by Dai Shi et al - a comprehensive survey on oversquashing and how to deal with it

Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search by Abbas Mehrabian, Ankit Anand, Hyunjik Kim, et al (feat Petar Veličković) - an excellent read on approaching one of the classical graph theory problems with RL