GraphML News (Nov 9th) - ELLIS PhD Applications, Protenix AF3, New papers

The next week is going to be busy with ICLR rebuttals, so we still have a bit of time to check the news and read new papers.

🎓 The call for PhD applications within ELLIS, the European network of ML and DL labs, is ending soon (November 15th) - this is a great opportunity to start (or continue) your academic journey in a top machine learning lab!

🧬 ByteDance released Protenix, a trainable PyTorch reproduction of AlphaFold 3, with model checkpoints (so you can run it locally) and with the inference server. The tech report is coming, would be interesting to compare with Chai-1 and other open source reproductions.

Weekend reading:

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing by Julia Balla et al feat Tess Smidt - introduces cool new graph datasets - given a point cloud of 5000 galaxies, predict their cosmological properties on the graph level and node level (eg, galaxy velocity).

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions by Anuroop Sriram and FAIR (NeurIPS 24): extension of FlowMM (ICML 2024), a flow matching model for crystal structure generation, but now instead of sampling from a Gaussian, the authors fine-tuned LLaMa 2 on the Materials Project to sample 10000 candidates (using just a small academic budget of 300 A100 gpus) - this prior yields 3x more stable structures.

Flow Matching for Accelerated Simulation of Atomic Transport in Materials by Juno Nam and MIT team feat. Rafael Gómez-Bombarelli - introduces LiFlow, a flow matching model for MD simulations of crystalline materials: where ab-initio methods would take 340 days to simulate 1 ns of a 200-atoms structure, LiFlow takes only 48 seconds 🏎️