vitrum 1.1.0

vitrum is a Python package designed for the generation, analysis, and simulation of disordered and glassy atomic structures. It provides a comprehensive suite of tools for structural characterization, diffusion analysis, and tools for machine learning-driven potential development.

🚧 Active development

vitrum is under active development. Before 2.0, a minor release may still remove or rename API that turned out to be wrong — every such change is listed in the changelog, and anything scheduled for removal is deprecated with a warning naming its replacement first where practical. From 2.0 onwards the public API follows semantic versioning.

📖 Documentation

Please see the docs folder for detailed documentation or check the online documentation.

📦 Installation

vitrum is available on PyPI:

pip install vitrum

To install dependencies for simulation workflows (atomate2, fireworks, jobflow):

pip install vitrum[workflows]

For the latest development version, clone the repository and install it in editable mode instead:

git clone https://github.com/R-Chr/vitrum.git
cd vitrum
pip install -e .

🚀 Examples

See the examples folder for runnable Jupyter notebooks demonstrating scattering/RDF analysis, Qn speciation, and random structure generation, among others.

🎯 Scope and Functionality

vitrum offers:

1. Structural Characterization

• Scattering Functions: Calculate partial and total Radial Distribution Functions (RDF) and Structure Factors ($S(q)$) for both Neutron and X-ray scattering (vitrum.scattering).
• Ring Analysis: Analyze ring size distributions and statistics in network glasses (vitrum.rings).
• Void/Cavity Analysis: Quantify free volume fraction and discrete cavity size distributions via a grid/probe-accessible-volume method (vitrum.voids).
• Topological Analysis: Compute persistent homology to identify medium-range order and topological features (vitrum.persistent_homology).
• Coordination & Angles: Analyze bond angle distributions and coordination environments (vitrum.coordination).

2. Dynamics & Diffusion

• Diffusion Analysis: Calculate Mean Squared Displacement (MSD), diffusion coefficients, and Van Hove correlation functions (vitrum.diffusion).

📑 Citation

If you use vitrum in your work, please cite it. Each GitHub release is archived on Zenodo with a version-specific DOI; see CITATION.cff for the citation metadata (GitHub's "Cite this repository" button uses this file automatically).

🤝 Contributing

Bug reports, test cases and pull requests are welcome. See CONTRIBUTING.md for the development setup and what a mergeable change looks like, and CODE_OF_CONDUCT.md for community expectations. Security issues should go through SECURITY.md rather than the public issue tracker.

👥 Author

Rasmus Christensen (rasmus.christensen.a1@tohoku.ac.jp)

⭐ Acknowledgements

vitrum relies on several powerful open-source packages:

• ASE
• Pymatgen
• NumPy / SciPy / pandas
• scikit-learn
• Dionysus / DioDe
• Atomate2 / Jobflow / Fireworks