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1/ What are key design principles for scaling neural network interatomic potentials? Our exploration leads us to top results on the Open Catalyst Project (OC20, OC22), SPICE, and MPTrj, with vastly improved efficiency! Accepted at #NeurIPS2024: arxiv.org/abs/2410.24169

I’m excited to share our latest work on generative models for materials called FlowLLM. FlowLLM combines Large Language Models and Riemannian Flow Matching in a simple, yet surprisingly effective way for generating materials. arxiv.org/abs/2410.23405 Benjamin Kurt Miller Ricky T. Q. Chen Brandon Wood

✨EXCITING NEWS! Registration for the 3rd Learning on Graphs conference is now open 😊 It is virtual, free to attend, livestreamed, and recorded 📹 Sign up today! forms.gle/eYiDCopJGUc8px…

Interested in the expressivity of higher-order attention? In our NeurIPS 2024 paper, we show that the Edge Transformer, originally designed for systematic generalization, has 3-WL expressivity and achieves SOTA performance on molecular regression and algorithmic reasoning tasks.

LoG 2024 paper decision notifications will be delayed until Saturday, 16 November. The notifications were originally meant to go out today AoE, but the committee need a few extra days to prepare the best program. Apologies to LoG 2024 authors.

Excited to unveil OCx24, a two-year effort with University of Toronto and @VSParticle! We've synthesized and tested in the lab hundreds of metal alloys for catalysis. With 685 million AI-accelerated simulations, we analyzed 20,000 materials to try and bridge simulation and reality. Paper:

Discovering and developing a novel material used to take 15-25 years and over $100M for a single materials system. Not anymore. Radical AI – the AI and Autonomy driven materials discovery company built for breakthrough technological innovations for the world’s most important

I shared a controversial take the other day at an event and I decided to write it down in a longer format: I’m afraid AI won't give us a "compressed 21st century". The "compressed 21st century" comes from Dario's "Machine of Loving Grace" and if you haven’t read it, you probably

Introducing All-atom Diffusion Transformers — towards Foundation Models for generative chemistry, from my internship with the FAIR Chemistry team FAIR Chemistry AI at Meta There are a couple ML ideas which I think are new and exciting in here 👇

1/ Machine learning force fields are hot right now 🔥: models are getting bigger + being trained on more data. But how do we balance size, speed, and specificity? We introduce a method for doing model distillation on large-scale MLFFs into fast, specialized MLFFs!

🚨 Checkout our new MD simulation package! ✅ Completely written in PyTorch ✅ Handles batched simulations ✅ Easy to add new MLIPs Led by Abhijeet Gangan and Orion Archer Cohen Spoiler alert: New MLIP coming soon! Code (MIT License): github.com/Radical-AI/tor…

Excited for the release of our EGIP model! Pushing the boundaries of efficiency and accuracy 🚀

ML-first materials are here: we demonstrate that machine learning approaches to materials prediction achieves state-of-the-art accuracy and best-in-class efficiency and performance. Adeesh Kolluru is leading the charge Radical AI

We are hiring across ML roles! A lot of exciting AI projects are going on Radical AI, feel free to reach out directly for any questions.

Excited to share our latest releases to the FAIR Chemistry’s family of open datasets and models: OMol25 and UMA! AI at Meta FAIR Chemistry OMol25: huggingface.co/facebook/OMol25 UMA: huggingface.co/facebook/UMA Blog: ai.meta.com/blog/meta-fair… Demo: huggingface.co/spaces/faceboo…