GraphML News (September 17th) - Chai-1, GenMS

🍓 This week offered a significant portion of strawberries that might result in major improvements in scientific applications. For now, let’s try to check what’s there beyond the berries.

🧬 Chai Discovery emerged from stealth and released Chai-1 - a reproduction of AlphaFold 3 with trained weights (thanks to a month on 128 A100 which saved you roughly $500k), a tech report, open inference server, and inference code (interestingly, no model code). Initial experiments report numbers close to AF 3. Chai is backed by OpenAI and many famous VCs, so it might appear as a new strong player in the industry, we’ll keep an eye.

🔮 Google DeepMind announced GenMS: Generative Hierarchical Materials Search by Sherry Yang, Simon Batzner, and the team that brought us UniMat last year. GenMS employs three components: (1) Gemini 1.5 to sample candidate formulae after a natural language query, eg, “give me the formula for a stable, chalcogenide with atom ratio 1:1:2 that's not in the ICSD database”. Samples are filtered through some rule-based heuristics and re-reranked by an LLM; (2) best candidates are sent to a diffusion model (non-equivariant, attention-based 3D Unet) to generate 3D structures; (3) the structures are scored by a pre-trained ML potential (NequIP) - if they are stable and exhibit target characteristics, we add them as a tree branch for the new iteration by LLMs. GenMS excels at perovskites, pyrochlore, and spinel crystals with structures confirmed by DFT formation energy calculations. Almost no geometric DL whatsoever 🙀

Weekend reading:

Recurrent Aggregators in Neural Algorithmic Reasoning by Kaijia Xu and Petar Veličković - the first model capable to solve quickselect from the CLRS benchmark happened to be a Triplet MPNN with a non permutation-invariant LSTM aggregator (GraphSAGE vibes). Back in January in our annual review post quickselect was the most unlikely candidate for traction, and looks like it is almost solved now!

On the design space between molecular mechanics and machine learning force fields by Yuanqing Wang and a huge collab of physicists and chemists led by NYU (feat. Kyunghyung Cho) - a nice intro to molecular mechanics, force fields, and potentials approachable by folks without a degree in physics. The survey includes a discussion on foundational ML potential models and “a nihilstic epilogue” worth checking out.