Victoria Gil
@mvictoriagil.bsky.social
Energy Proceses and Emission Reduction (PrEM) Group at CSIC-INCAR
Reposted by Victoria Gil
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
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
March 12, 2025 at 9:18 PM
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
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
Reposted by Victoria Gil
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'
#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
March 6, 2025 at 9:08 AM
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'
#OpenAccess 🔓
pubs.rsc.org/en/content/a...
Reposted by Victoria Gil
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! 👇
📝 Paper: pubs.rsc.org/en/content/a...
💻 JupyterHub: t1p.de/matextract-cpu
📚 Online book: matextract.pub
📽️ intro from @kjablonka.com! 👇
January 3, 2025 at 8:46 AM
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! 👇
📝 Paper: pubs.rsc.org/en/content/a...
💻 JupyterHub: t1p.de/matextract-cpu
📚 Online book: matextract.pub
📽️ intro from @kjablonka.com! 👇
Reposted by Victoria Gil
🎅🏼 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
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
December 21, 2024 at 10:39 PM
🎅🏼 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
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