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We wrapped up the year with the traditional TCP Christmas workshop: 10 talks (4 invited), followed by a dinner celebration. It was wonderful to have Tristan Bereau, Kevin M Jablonka, Francesco Bongiovanni (@LuxProvide), and Stefan Vučković join us. Happy holidays everyone!🎄

Check out "From text to insight: large language models for chemical data extraction" by Kevin M Jablonka & colleagues! Jablonka Lab (Lab for AI for Materials) Read the #OpenAccess Tutorial review here 👇 pubs.rsc.org/en/content/art…

Yay, our recent J. Am. Chem. Soc. paper was highlighted in the news 🙃 #NSFfunded #DMREF pubs.acs.org/doi/10.1021/ja…

I am looking for an elite Head of Computational Materials to join Radical AI. If you know you are one of the best in the world at what you do, please text or email me. We are building the future of scientific research.

Many of our benchmarks underwent a large revision in the last weeks. We now host HuggingFace spaces for them and the dataset. The revised article and post below give more details

Happy #WomensDay to our awesome party planners Mara, Meiling and Anagha! 🎉 The real MVPs who keep our AI materials lab both smart AND fun! You three rock! #LabLife #WorkHardPartyHarder 🥂

🚀ChemBench just leveled up! We’re thrilled to announce the latest release of ChemBench—now smarter and smoother! Dive into benchmarking any chemistry AI model with our revamped framework, designed for flexibility and ease. #ChemistryAI #MachineLearning #OpenScience #Innovation

Very happy to announce that I'll be joining the University of Tübingen (Universität Tübingen) as a Full Professor (W3) of "Machine Learning for Science" in the Computer Science Department within the Faculty of Science and the #ExcellenceCluster ML4Science. Tübingen is an exceptionally

Day 1 of the Foundation Models workshop hosted by the ELLIS Winter School!

Here's a good read if you're working on the eval side of AI (like myself), or even if you're looking to read!! 📜 Work from Kevin M Jablonka group ✅ arxiv.org/pdf/2503.10837

ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models "We present the ChemPile, an open dataset containing over 75 billion tokens of curated chemical data, specifically built for training and evaluating general-purpose models in the chemical sciences."

ChemPile: A 250 GB Diverse and Curated Dataset for Chemical Foundation Models １．This paper introduces ChemPile, the largest open-source, expertly curated dataset (250 GB, 76.7B tokens) tailored for training foundation models in chemistry—addressing the current lack of diverse,

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures

Title grew longer, the benchmark shrank: ChemBench is now out today in Nature Chemistry. rdcu.be/emNWM chembench.lamalab.org behind the paper: communities.springernature.com/posts/behind-t…

A much needed chemistry dataset! Nice work!