Ever get tired of tiny timesteps bottlenecking your MD simulations? We show how to train a model for large-timestep Hamiltonian dynamics directly on standard MLFF datasets. 𝗡𝗼 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝗶𝗲𝘀, 𝗻𝗼 𝘂𝗻𝗿𝗼𝗹𝗹𝗶𝗻𝗴, 𝗻𝗼 𝘁𝗲𝗮𝗰𝗵𝗲𝗿 needed! 🧵👇

If you ever wondered about the pros and cons of Euclidean symmetries in generative models for molecules, stop by at our #NeurIPS poster on Thursday morning 🧬 #AI4Science

SO3LR is now published in @jacs.acspublications.org ☀️ It’s a pre-trained #ML #forcefield, applicable to #proteins, #glycoproteins and #lipids in explicit water or vacuo. 🧬 Test it right now and run you own simulations 👉 github.com/general-mole... #AI4Science #MachineLearning

The room I was given to teach #PhysicalChemistry @uoft.bsky.social has no chalkboards 😱 - o tempora, o mores! But then I can just as well record and share my lectures here: youtube.com/playlist?lis...

Microsoft researchers introduce MatterGen, a model that can discover new materials tailored to specific needs—like efficient solar cells or CO2 recycling—advancing progress beyond trial-and-error experiments. www.microsoft.com/en-us/resear...

Excited to share our latest work on Euclidean fast attention, which enables learning global atomic representations at linear cost! 🔥 The representations describe the distance and orientation between atoms, crucial for modeling molecular systems tinyurl.com/47xud8nr #MachineLearning #AI4Science

The first list filled up, so here's a second list of AI for Science researchers on bluesky. Let me know if I missed you / if you'd like to join! bsky.app/starter-pack...