The SAFE-MolGen is LLM-driven agentic system, where we augmented large language models
(LLMs)
using datasets from SAFE, cheminformatics tools and supervised machine learning (ML) models to
generate new
extractants and
separation conditions for
f-element solvent extraction separation. The full paper has been
published. The SAFE-MolGen website here allows researchers to directly use our workflow.
If you're
still
interested in
implementation details, welcome to check SAFE-MolGen
GitHub
repository.
🚀 How to Use Our Service
-
Log in to the SAFE website at
https://safe.lanl.gov/login
.
If you don't have an account yet, please register first at
https://safe.lanl.gov/signup
.
-
Go to the SAFE-MolGen website:
https://safe.lanl.gov/molgen
.
-
On the website, choose your preferred LLM and provide the corresponding API
key.
- If you select a GPT model, use an API key from the OpenAI platform.
- If you choose a Llama model, the key should come from your NERSC account (know more NERSC).
We provide video guides for both options on the website. Note that we do not store your API key.
-
You can enter your parameters in the input fields provided
to run the workflow.
- Click the "Generate New Extractants" button to start the generation process.
- Optionally:
- Click "Preview Experimental Evaluation Table for Prompt" to view the evaluation
data
and associated DOIs.
- Click "Preview LLM's Prompt" to see the LLM prompt that will be sent to the
model.
- Please wait for ~10 minutes as the SAFE-MolGen will generate and
evaluate the
new
extractants.
- Once completed, you will receive your results in your registered email. You can download your
results (including New Extractants and corresponding
experimental
separation conditions) and parameters using the provided download button.
📺 More Resources
To know more of how SAFE-MolGen works:
🖥️ Website Implementation
The SAFE-MolGen website is implemented using NERSC SPIN to host the web UI. The open
source llama
model weights are stored in global common of Perlmutter HPC and the LLM is executed via Perlmutter
HPC GPU-based LLM inference via batch jobs. Maintaining up-to-date documentation of this
project is
important based on NERSC
appropriate use policy documentation and is reponsible for Separation ML
team.
📄 About SAFE-MolGen
- About us: Accelerating f-element
separation with machine learning
- Contact us: Contact Page
- Cite our work: Zhang, B.; Summers, T. J.; Augustine, L. J.; Taylor, M. G.; Geist, A.; Li, R.;
Batista, E. R.; Perez, D.; Yang, P.; Schrier, J. Augmenting Large Language Models for Automated
Discovery of f-Element Extractants. J. Am. Chem. Soc. 2026. DOI:
https://doi.org/10.1021/jacs.5c19738