https://github.com/jan-janssen/langsim
Application of Large Language Models (LLM) for computational materials science - visit jan-janssen.com/LangSim
https://github.com/jan-janssen/langsim
atomistic-simulations computational-materials-science langchain large-language-models
Last synced: about 2 months ago
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Application of Large Language Models (LLM) for computational materials science - visit jan-janssen.com/LangSim
- Host: GitHub
- URL: https://github.com/jan-janssen/langsim
- Owner: jan-janssen
- License: bsd-3-clause
- Created: 2024-05-02T13:54:14.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-23T13:58:47.000Z (7 months ago)
- Last Synced: 2025-04-02T10:44:38.130Z (about 2 months ago)
- Topics: atomistic-simulations, computational-materials-science, langchain, large-language-models
- Language: Jupyter Notebook
- Homepage: http://jan-janssen.com/LangSim/
- Size: 41.8 MB
- Stars: 58
- Watchers: 5
- Forks: 12
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Run Calculations with a Large Language Model
[](https://mybinder.org/v2/gh/jan-janssen/LangSim/HEAD?labpath=notebooks/demonstration.ipynb)
[](https://hub.docker.com/r/ltalirz/LangSim)
The computational chemistry and computational materials science community have both developed a great number of
simulation tools. Still these tools typically require either rather cryptic input files or at least a fundamental
programming experience in a language like Python to control them. Furthermore, many questions are only answered in the
documentation, like:
* Which physical units does the code use?
* Which inputs match to which variables in the equations in the paper?
* ...
We address this challenge by developing a Large Language Model (LLM) extension which provides LLM agents to couple the
LLM to scientific simulation codes and calculate physical properties from a natural language interface.
## Installation
### Via pip
While our package is not yet available on the Python Package Index, you can install it directly using:
```
pip install git+https://github.com/jan-janssen/LangSim.git
```
The pip package includes optional dependencies for the `mace` model and the `jupyter` integration.
### Via conda
As the conda package is not yet available on Anaconda.org still you can clone the repository and install the
dependencies directly from conda using the [environment.yml](environment.yml) file.
Prerequisites:
- [git](https://git-scm.com/)
- [conda](https://docs.conda.io/en/latest/miniconda.html)
```bash
git clone https://github.com/jan-janssen/LangSim
cd LangSim
conda env create -f environment.yml --name LangSim
```
### Via Docker Container
We build a docker container on every commit to the `main` branch.
You can pull the container from the [Docker Hub](https://hub.docker.com/r/ltalirz/langsim) using:
```bash
docker run -p 8866:8866 ltalirz/langsim
```
## Using the package
The package currently provides two interfaces, one for python / jupyter users to query the large language model directly
from a python environment and a second web based interface.
## Contributors
* [Yuan Chiang](https://github.com/chiang-yuan)
* [Giuseppe Fisicaro](https://github.com/giuseppefisicaro)
* [Jan Janssen](https://github.com/jan-janssen)
* [Greg Juhasz](https://github.com/gjuhasz)
* [Sarom Leang](https://github.com/saromleang)
* [Bernadette Mohr](https://github.com/Bernadette-Mohr)
* [Utkarsh Pratiush](https://github.com/utkarshp1161)
* [Francesco Ricci](https://github.com/fraricci)
* [Leopold Talirz](https://github.com/ltalirz)
* [Pablo Andres Unzueta](https://github.com/pablo-unzueta)
* [Trung Vo](https://github.com/btrungvo)
* [Gabriel Vogel](https://github.com/GaVogel)
* [Luke Zondagh](https://github.com/Luke-Zondagh)
* [Sebastian Pagel](https://github.com/pagel-s)