Post #792

Graph ML News (Sep 16th) - Breakthrough Prize, OpenCatalyst cases, Illustrated Cats, EvoDiff 🏆 The Breakthrough Prize winners aka “Oscars of Science” were announced earlier this week (Ig Nobel Prizes were announced as well but that’s a story for another fun time) and they do have a nice connection to Geometric DL! The Math prize went to Simon Brendle (Columbia) for “transformative contributions to differential geometry, including sharp geometric inequalities, many results on Ricci flow and mean curvature flow and the Lawson conjecture on minimal tori in the 3-sphere.” Ricci flows played a key role in understanding theoretical capabilities of GNNs in the seminal paper by Topping et al that received ICLR 2022 Outstanding Award and spun off more research of differential geometry and GNNs. Perfect time to jump on the Ricci flowwagon (pun intended). Do check other winners, their research is very cool as well. 🧪 The OpenCatalyst team published two case studies how the OCP demo helped in the scientific research of catalyst discovery: for the nitrogen reduction reaction (NRR) and for hydrogen fuel cells. OpenCatalyst turns into smth like AlphaFold but for materials science and chemistry. 😼 Finally, check out the Category Theory Illustrated book by Boris Marinov - this perhaps the most visual resource to understand the basics of Category Theory. As of now, 6 chapters are ready — on Sets, Categories, Monoids, Order, Logic, and Functors. Don’t forget about Cats4AI to learn more about Category Theory applied to ML and GNNs. 🧬 MSR AI4Science released EvoDiff - a massive work on the discrete diffusion generative model for conditional generation of protein sequences. EvoDiff was designed for sequences and MSAs and ships in two sizes — 38M and 640M params so it would fit on a variety of GPUs. Some weekend reading: Protein generation with evolutionary diffusion: sequence is all you need - introducing EvoDiff Graph Neural Networks Use Graphs When They Shouldn't by Bechler-Speicher et al. - one more evidence for graph rewiring Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network? by Qin et al - for all you hardcore theory lovers on the channel