README

MADMAN (MAterial Data Mining and ANalysis) is a Python library designed to facilitate the work of material scientists by providing tools for data analysis and modeling.

This version includes:

• the analysis module, which defines classes for handling various types of electron spectra, particularly useful in solar cell detailed-balance models;

• the helpers module, which contains density-functional-theory workflows based on the Atomistic Simulation Environment and GPAW, which are routinely used in my research.

More functionality will be added as soon as new modules are developed.

MADMAN is published on Zenodo with DOI 10.5281/zenodo.14502854.

Installation

You can install MADMAN and its dependencies by executing:

pip install .

from its root directory, containing the setup.py file. Dependencies should be automatically installed by pip, but you can find their list in the requirements.txt.

Usage

The code has been written to be as easy to understand as possible. Studying the aforementioned references and the API documentation stored in the /docs/ folder should be enough to use it in an informed and appropriate way.

Examples may be found in the /tests/ folder.

More detailed documentation on the methods might be added in the future.

Acknowledgement

The software has been developed in the context of the research project PhANTOM (Empowering Photovoltaics with Intermediate Band Chalcogenides: From Atoms to New Tailored Optoelectronic Materials), funded by the Italian Ministry of University and Research (MUR) through the National Recovery and Resilience Plan (PNRR) with the call Young Researchers - Seal of Excellence (CUP number: E13C22002920006).

CHANGES

0.0.1-alpha - 19 Dec 2024

• Added: Initial release with basic functionality.
• Known issues: Some features may not work as expected. Please report any bugs (matteo.cagnoni@polito.it).

LICENSE

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Copyright © 2024 Matteo Cagnoni (matteo.cagnoni@polito.it)

Conditions

You are free to copy and redistribute the material in any medium or format under the following terms:

• Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

• NonCommercial: You may not use the material for commercial purposes.

• NoDerivatives: If you remix, transform, or build upon the material, you may not distribute the modified material.

For the full text of the license, visit the official Creative Commons website.

Disclaimer

The software is provided "as is", without warranty of any kind, expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement.

In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.