This project implements a Language Learning Model (LLM) that simulates a material scientist with deep knowledge of organic materials and molecular interactions. The LLM is designed to analyze functional groups, their interactions, and propose new hypotheses based on given data.
This project includes a Streamlit app as the frontend interface. Streamlit provides an easy-to-use framework for creating web applications with Python.
run the app
streamlit run app.py
To install this project, follow these steps:
- Ensure you have Python 3.11 installed on your system.
- Install Poetry if you haven't already. You can find installation instructions at https://python-poetry.org/docs/#installation.
- Clone this repository to your local machine:
git clone [your-repository-url] cd [your-project-directory] - Install the project dependencies using Poetry:
poetry install --no-root
This command will create a virtual environment and install all the necessary dependencies without installing the project itself as a package.
User can upload a data file and knowledge file. The LLM will analyze the data and provide insights based on its knowledge of organic materials and molecular interactions.
- Implement analysis for molecular interactions as research assistant, capable of online semantic search.
- Add in data intake from csv function.
- Add in knowledge base prompt engineering refinement
- Implement a more agents for more complex analysis and query-retrieval based hypothesis generation (human in the loop)
- Enhance the Streamlit frontend with visualizations of molecular structures and data
- Integrate machine learning models for predictive analysis of material properties
- Integrate robotic operation sent our request to the lab for experimentation.
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