Metadata-Version: 2.4
Name: ct_predictor
Version: 0.1.0
Summary: Neural network predictor of the occupation of the density of states in doped disordered electronic materials
Author: Melissa Berteau-Rainville, Emanuele Orgiu, Ingo Salzmann
License: CC BY-NC-ND 4.0
Project-URL: Paper, https://doi.org/10.1038/s41524-026-XXXX-X
Project-URL: Homepage, https://github.com/yourusername/ct_predictor
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: Free for non-commercial use
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: onnxruntime>=1.15.0
Requires-Dist: numpy>=1.21.0
Dynamic: license-file

# Copyright (c) 2026 Melissa Berteau-Rainville, Emanuele Orgiu, Ingo Salzmann
# This work is licensed under CC BY-NC-ND 4.0. 
# To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

## Citation

If you use **ct_predictor** in your research, please cite the following paper:

> Berteau-Rainville, M., et al. (2026). "Title of the Paper." *Journal Name*, Volume(Issue), pages. DOI: [10.1038/s41524-026-XXXX-X](https://doi.org/10.1038/s41524-026-XXXX-X)

```bibtex
@article{BerteauRainville2026,
  title={Title of the Paper},
  author={Berteau-Rainville, Melissa and Co-author, A. and Co-author, B.},
  journal={Journal Name},
  year={2026},
  doi={10.1038/s41524-026-XXXX-X},
  url={[https://github.com/yourusername/ct_predictor](https://github.com/yourusername/ct_predictor)}
}

## Support and Contact

If you have questions regarding the **software implementation**, bug reports, or feature requests, please:
* **Open an Issue:** Use the [GitHub Issue Tracker](https://github.com/yourusername/ct_predictor/issues).
* **GitHub Profile:** Reach out via the email listed on the maintainer's GitHub profile.

For inquiries regarding the **underlying research, methodology, or theoretical framework**, please direct your queries to the **corresponding author** of the associated paper:
* **Paper:** [Title of your Published Paper]
* **DOI:** [10.1038/s41524-026-XXXX-X](https://doi.org/10.1038/s41524-026-XXXX-X)

# ct_predictor

Predict charge transfer properties from CSV input using a neural network stored in ONNX format.

## Installation

```
pip install ct_predictor
```

## Usage

```python
from ct_predictor.predictor import MLPredictor

p = MLPredictor(output_path="results.csv")
p.predict("inputs.csv")
```
