Victoria Gil
@mvictoriagil.bsky.social
Energy Proceses and Emission Reduction (PrEM) Group at CSIC-INCAR
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...
From text to insight: large language models for chemical data extraction
The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on m...
pubs.rsc.org
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! 👇
From text to insight: large language models for chemical data extraction
The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on m...
pubs.rsc.org
Kevin Jablonka@kjablonka.com · 2y ago🎅🏼 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
🎅🏼 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
From text to insight: large language models for chemical data extraction
The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on m...
pubs.rsc.org
