What if AI could invent enzymes that nature hasn’t seen? 👩‍🔬🧑‍🔬 Introducing 🪩 DISCO: Diffusion for Sequence-structure CO-design 📝 Blog: disco-design.github.io 📄 Paper: arxiv.org/abs/2604.05181 💻 Code: github.com/DISCO-design...

📄📄📄 The AI4Mat-NeurIPS-2025 workshop is now open for submissions until August 22, 2025 (AOE)! Consider submitting full-length papers or shorter-length findings. We also have a special track for papers on benchmarking AI for materials design. sites.google.com/view/ai4mat/...

Thanks to all the speakers and participants for the engaging discussions today, and for making the AI4Mat@ICLR 2025 workshop a great success! Thanks also to Santiago, @martaowesyou.bsky.social, and the rest of the co-organizing team for the effort putting this together. Great to be a part of it!

🚀 Looking for reaction conditions that work well for multiple substrates? CurryBO can help🍛 Now out on arXiv: arxiv.org/abs/2502.18966 A short explanation thread 👇

One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions

General parameters are highly desirable in the natural sciences - e.g., chemical reaction conditions that enable high yields across a range of related transformations. This has a significant practical...

arxiv.org

We at @digital-discovery.bsky.social are very happy to announce a new paper type called "Commit". Inspired by version control systems such as git, the idea is that if you have an update on a short and pointed publication, you can send it as a commit. We envision commits to be co-cited with the

Excited to have #selfdrivinglaboratories listed as one of the seven technologies to watch in 2025 by @nature.com Thanks to the #matterlab, the @accelerationc.bsky.social and of course all the global community on SDLs! @uoft.bsky.social @vectorinst.bsky.social l www.nature.com/articles/d41...

Self-driving laboratories, advanced immunotherapies and five more technologies to watch in 2025

Sustainability and artificial intelligence dominate our seventh annual round-up of exciting innovations.

nature.com

"The Superposition of Diffusion Models using the Îto Density Estimator" (@martaowesyou.bsky.social, @lazaratan.bsky.social et al.) It's nice to see an easy-to-compute log-likelihood estimator for SDE sampling of diffusion models (not just ODE) 📄 arxiv.org/abs/2412.17762 🐍 github.com/necludov/sup...

New paper just dropped! How do you combine pre-trained diffusion models without having to train a new one 🤓? Turns out you can use our all new Ito density estimator 🔥 to compute densities under a diffusion model efficiently 🚀!

Kirill Neklyudov@k-neklyudov.bsky.social · 2y ago

🧵(3/7)This is all due to an amazing team: @martaowesyou.bsky.social @lazaratan.bsky.social @joeybose.bsky.social @alextong.bsky.social 📄Paper: arxiv.org/abs/2412.17762 💻Code: github.com/necludov/sup... 🤗HuggingFace: huggingface.co/superdiff

Super super excited to share our work SuperDiff 🦹‍♀️ for superimposing pretrained diffusion models at inference time 💪 Check out the 🧵 to see how we superimposed proteins as well as images, all thanks to a fast new density estimator. Curious to see what 🍩 & 🗺️ would produce?

Kirill Neklyudov@k-neklyudov.bsky.social · 2y ago

🧵(1/7) Have you ever wanted to combine different pre-trained diffusion models but don't have time or data to retrain a new, bigger model? 🚀 Introducing SuperDiff 🦹‍♀️ – a principled method for efficiently combining multiple pre-trained diffusion models solely during inference!

exciting new workshop announcement!! join us in Singapore for Frontiers in Probabilistic Inference: Learning Meets Sampling 🌏⚡️😃 details below 👇 #ICLR2025

Joey Bose@joeybose.bsky.social · 2y ago

🔊 Super excited to announce the first ever Frontiers of Probabilistic Inference: Learning meets Sampling workshop at #ICLR2025 @iclr-conf.bsky.social! 🔗 website: sites.google.com/view/fpiwork... 🔥 Call for papers: sites.google.com/view/fpiwork... more details in thread below👇 🧵

1/3 🧵Quantum conundrum: we want expressive circuits, but current hardware only allows short coherence times, and so more parameters = more problems. Check out our #NeurIPS2024 paper “Quantum Deep Equilibrium Models”.