LLMs are pretty bad at writing molecules, but quite good at analyzing mols and reactions! In our new work we use LLMs+search in chemical tasks, unlocking steerable synth. planning and mechanism prediction 🌟 Chehck out the paper 👉 arxiv.org/abs/2503.08537 1/8

From @kjablonka.com, @mvictoriagil.bsky.social, @pepe-marquez.bsky.social and colleagues. 'From text to insight: large language models for chemical data extraction' #OpenAccess 🔓 pubs.rsc.org/en/content/a...

Our data extraction tutorial is now online in Chem. Soc. Rev. The notebooks can be run using the #jupyter4nfdi service from #base4nfdi. 📝 Paper: pubs.rsc.org/en/content/a... 💻 JupyterHub: t1p.de/matextract-cpu 📚 Online book: matextract.pub 📽️ intro from @kjablonka.com! 👇

🎅🏼 A small early Christmas present from our team. To celebrate the publication of our data extraction tutorial in Chem Soc Rev, we made it easy to run it — without any installation — on a JupyterHub of the Base4NFDI. 🎥 Video intro to the JupyterHub deployment: youtu.be/l-5QNUo1fcU