Post #818

​​GraphML News (Jan 13th) - New material discovered by geometric models, LOWE What time could be better than the time in between ICLR announcements (Jan 15th) and the ICML deadline (Feb 1st) 🫠. As far as we know, the graph community is working on some huge blog posts - you can expect those coming in the next few days. The two big news from this week: Microsoft Azure Quantum together with Pacific Northwest National Lab announced successful synthesis and validation of a potentially new electrolyte candidate suitable for solid-state batteries. The fresh accompanying paper describes the pipeline from generating 32M candidates and stepwise filtering of those down to 500K, 800, 18, and 1 final candidate. The main bulk of the job of filtering millions of candidates was done by the geometric ML potential model M3GNet (published in 2022 in Nature Computational Science) while later stages with a dozen candidates included HPC simulations of molecular dynamics. Geometric DL for materials discovery is rising! 🚀 Valence & Recursion announced LOWE (LLM-orchestrated Workflow Engine). LOWE is an LLM agent that strives to do all things around drug discovery - from screening and running geometric generative models to the procurement of materials. Was ChemCrow🐦‍⬛ the inspiration for LOWE? Weekend reading: Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation by Chen, Nguyen, et al - the paper behind the newly discovered material by Azure Quantum and PNNL. MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules by Kovács, Moore, et al - similarly to MACE-MP-0 from the last week, MACE-OFF23 is a transferable ML potential for organic molecules but smaller - Medium and Large models were trained on a single A100 for 10/14 days. Improved motif-scaffolding with SE(3) flow matching by Yim et al - the improved version of FrameFlow (based on trendy flow matching), originally for protein backbone generation, to motif-scaffolding. On some benchmarks, new FrameFlow is on par or better than mighty RFDiffusion 💪