SolarCell-ML-Applications

Applying machine learning to analyze solar cell performance and predict efficiency.

Overview

This repository contains Jupyter notebooks, environment specifications, and data used to explore and model solar cell performance using machine learning methods. The goals are:

• Reproduce experiments and analyses.
• Provide notebooks that walk through data ingestion, feature engineering, model training, and evaluation.
• Offer reusable environment files to reproduce results.

Getting started

1. Clone the repo:

git clone https://github.com/maltiwainy229/SolarCell-ML-Applications.git
cd SolarCell-ML-Applications

Environments

The emvironments folder contains environment definitions that let users reproduce the package setup used for running the notebooks.

Data

See data for data sources.

Notebooks

notebooks - each notebook contains markdown descriptions and explanations, see in-file comments and docstrings for more details in source scripts. Run the notebooks top-to-bottom to reproduce results.

Model File

Due to GitHub size limits, the full pickle file (~100 GB) is hosted externally: Download from Google Drive

Contributing

Contributions are welcome!

If you plan to contribute code or updated notebooks:

• Create a feature branch from main.
• Keep notebooks reproducible: clear output cells before committing or use nbstripout.
• Run notebooks end-to-end before opening a PR.

Credits & Resources

This project builds on and references the following repositories and resources:

• alvarovm/solarcelldata — https://github.com/alvarovm/solarcelldata
• edbeard/chemdataextractor-uvvis2018 — https://github.com/edbeard/chemdataextractor-uvvis2018