Metadata-Version: 2.4
Name: openmc-optimizer
Version: 0.1.1
Summary: Genetic and Bayesian optimization workflows for OpenMC models
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scikit-learn<2,>=1.4
Provides-Extra: test
Requires-Dist: pytest<10,>=8; extra == "test"
Dynamic: license-file

# OpenMC Optimizer

OpenMC Optimizer is a Python toolkit for optimizing OpenMC reactor models. It
provides bounded genetic algorithm and constrained Bayesian optimization
methods to minimize the power peaking factor (PPF), with an optional
k-effective target and tolerance constraint. Design variables are independent
of the search method. The built-in material adapter can vary an element or
nuclide fraction in a material, or mix two material recipes by volume. These
generic operations support a wide range of material studies, including fuel
enrichment and absorber loading.

## Install

Use an environment with the OpenMC Python package and executable available.
Then install this project:

```bash
python -m pip install -e .
```

Install the released package:
```bash
pip install openmc-optimizer
```

Scikit-learn is installed as a required dependency, so both optimization
methods are available after the standard install.

## Documentation

For detailed user guild please visit: [OpenMC Optimizer Documentation](https://qyxen.github.io/openmc-optimizer/)

## Citation
Cite this project using the metadata in [CITATION.cff](CITATION.cff).
