semvec/pypi-packages huggingface dataset:
mkite-db 25.11.3
mkite: distributed computing platform for high-throughput materials simulations
Requires Python: >=3.8
Keywords: workflow, materials-science

<div align="center">
  <img src="https://raw.githubusercontent.com/mkite-group/mkite_db/main/docs/_static/mkite-logo.svg" width="500"><br>
</div>

# What is mkite?

mkite is a suite of tools for running high-throughput materials simulations in distributed computing platforms.
The mkite suite decouples the production database from client workers, facilitating scaling of simulations across heterogeneous computing environments.
The infrastructure enables exploration of combinatorial materials spaces using workflows, recipes, data visualizations, and more.

Some advantages of mkite:

- It stores and organizes complex materials workflows on databases. For example, mkite allows creating workflows with more than one parent input branch (say, interfaces between solids and molecules).
- The server is agnostic to the computing environments where the tasks are performed, and the clients are unaware of the production database. This facilitates distributing the tasks across heterogeneous computing systems.
- It provides textual descriptions for workflows, and enables adapting them on-the-fly. This helps as a "lab notebook" for computational materials scientists.
- It is adaptable to many software packages and inputs. The recipe system also interacts well with other libraries, such as ASE, pymatgen, cclib, and more.

## Documentation

General tutorial for `mkite` and its plugins are available in the [main documentation](https://mkite.org).
Complete API documentation is pending.

## Installation

To install `mkite_db`, first install `mkite_core` and `mkite_engines`.
Then, install this repository with pip:

```bash
pip install mkite_core mkite_engines
pip install mkite_db
```

Alternatively, for a development version, clone this repo and install it in editable form:

```bash
pip install -U git+https://github.com/mkite-group/mkite_db
```

## Contributions

Contributions to the entire mkite suite are welcomed.
You can send a pull request or open an issue for this plugin or either of the packages in mkite.
When doing so, please adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) in the mkite suite.

The mkite package was created by Daniel Schwalbe-Koda <dskoda@ucla.edu>.

### Citing mkite

If you use mkite in a publication, please cite the following paper:

```bibtex
@article{mkite2023,
    title = {mkite: A distributed computing platform for high-throughput materials simulations},
    author = {Schwalbe-Koda, Daniel},
    year = {2023},
    journal = {arXiv:2301.08841},
    doi = {10.48550/arXiv.2301.08841},
    url = {https://doi.org/10.48550/arXiv.2301.08841},
    arxiv={2301.08841},
}
```

## License

The mkite suite is distributed under the following license: Apache 2.0 WITH LLVM exception.

All new contributions must be made under this license.

SPDX: Apache-2.0, LLVM-exception

LLNL-CODE-848161
mkwind 25.11.3
mkwind is a job building/submission automator companion to mkite
Requires Python: >=3.8
Keywords: workflow, materials-science

<div align="center">
  <img src="https://raw.githubusercontent.com/mkite-group/mkwind/main/docs/_static/mkwind.png" width="500"><br>
</div>

# What is mkwind?

mkwind is an accompanying package to mkite for running tasks on client workers.
This package allows running simulations without installing all the database requirements, thus protecting the server from modifications on the client side.
mkwind offers a set of tools for building, running, and postprocessing the tasks, and sending them back to mkite through `mkite_engines`.

## Documentation

General tutorial for `mkite` and its plugins are available in the [main documentation](https://mkite.org).
Complete API documentation is pending.

## Installation

To install `mkwind`, install the main dependencies `mkite_core` and `mkite_engines`:

```bash
pip install mkite_core mkite_engines
pip install mkwind
```

Alternatively, for a development version, use:

```bash
pip install -U git+https://github.com/mkite-group/mkwind
```

## Contributions

Contributions to the entire mkite suite are welcomed.
You can send a pull request or open an issue for this plugin or either of the packages in mkite.
When doing so, please adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) in the mkite suite.

The mkite package was created by Daniel Schwalbe-Koda <dskoda@ucla.edu>.

## License

The mkite suite is distributed under the following license: Apache 2.0 WITH LLVM exception.

All new contributions must be made under this license.

SPDX: Apache-2.0, LLVM-exception

LLNL-CODE-848161
phaseforgeplus 0.1.0
A computationally efficient, fully open-source workflow for physically-informed CALPHAD model generation and parameter fitting.
Requires Python: >=3.11

<div align="center">

# PhaseForge+

![Python](https://img.shields.io/badge/python-3.11-blue)
![Platforms](https://img.shields.io/badge/platform-linux%20%7C%20macos%20%7C%20windows-lightgrey)
[![Test](https://github.com/dogusariturk/PhaseForgePlus/actions/workflows/tests.yml/badge.svg)](https://github.com/dogusariturk/PhaseForgePlus/actions/workflows/tests.yml)

`PhaseForgePlus` is a Python-based, fully open-source workflow for generating and tuning physically-informed CALPHAD models. It integrates Machine-Learned Interatomic Potentials (MLIPs), the Alloy Theoretic Automated Toolkit (ATAT), and experimental data to efficiently produce accurate phase diagrams.

<p>
  <a href="https://github.com/dogusariturk/PhaseForgePlus/issues/new?labels=bug">Report a Bug</a> |
  <a href="https://github.com/dogusariturk/PhaseForgePlus/issues/new?labels=enhancement">Request a Feature</a>
</p>

</div>

---

## Features

* Automatic construction of CALPHAD models from MLIP-based thermodynamic data
* Integration with ATAT and PyCalphad for Gibbs energy and phase diagram calculations
* Efficient parameter fitting using the Jansson derivative method with gradient-based optimization
* Support for physically-grounded adjustments using experimental phase equilibria
* Compatibility with ESPEI and PyCalphad toolchain for advanced thermodynamic modeling

---

## Relationship to PhaseForge

`PhaseForgePlus` builds upon the foundation of [`PhaseForge`](https://github.com/dogusariturk/PhaseForge), extending its capabilities for advanced CALPHAD model optimization. While `PhaseForge` enables users to automatically generate initial thermodynamic database (TDB) files from MLIPs, `PhaseForgePlus` takes the workflow a step further by providing robust tools for parameter fitting, integration with experimental data, and gradient-based optimization.

You can use `PhaseForge` to create an initial `.tdb` file, which serves as the starting point for further optimization in `PhaseForgePlus`.

By combining `PhaseForge` and `PhaseForgePlus`, you can seamlessly transition from automated TDB generation to advanced model optimization in a single, reproducible workflow.

---

## Installation

You can install `PhaseForgePlus` via pip (once available on PyPI):

```sh
pip install phaseforgeplus
```

Or clone the repository directly:

```sh
pip install git+https://github.com/dogusariturk/PhaseForgePlus.git
```

---

## Quick Start

Here's a minimal example of optimizing a CALPHAD model using `PhaseForgePlus`:

```python
from phaseforgeplus import PhaseForgePlus

pfp = PhaseForgePlus(
    db="./data/pt-w.tdb",  # Path to your thermodynamic database
    zpf_path="./data",  # Path to Zero Phase Fraction (ZPF) data
    points=[1801, 1601, 1401, 1201, 1001, 802, 602, 402, 202],  # Points for optimization
    pressure=101325,  # Pressure in Pa
    temperature=298.15,  # Temperature in K
)

pfp.optimize()
```
mkite-vasp 25.11.3
mkite_vasp is a plugin to execute VASP calculations using mkite
Requires Python: >=3.8
Keywords: workflow, materials-science

# mkite_vasp

`mkite_conformer` is a plugin that helps running DFT calculations using [VASP](https://vasp.at/).
The plugin uses [pymatgen](https://github.com/materialsproject/pymatgen) and [custodian](https://github.com/materialsproject/custodian) to create the VASP inputs using Python, as well as running and monitoring the VASP calculation.
As an example, this plugin implements recipes such as relaxation and single-point calculations.

## Documentation

General tutorial for `mkite` and its plugins are available in the [main documentation](https://mkite.org).
Complete API documentation is pending.

## Installation

To install `mkite_vasp`, use pip:

```bash
pip install mkite_vasp
```

Alternatively, for a development version, clone this repo and install it in editable form:

```bash
pip install -U git+https://github.com/mkite-group/mkite_vasp
```

## Contributions

Contributions to the entire mkite suite are welcomed.
You can send a pull request or open an issue for this plugin or either of the packages in mkite.
When doing so, please adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) in the mkite suite.

The mkite package was created by Daniel Schwalbe-Koda <dskoda@ucla.edu>.

### Citing mkite

If you use mkite in a publication, please cite the following paper:

```bibtex
@article{mkite2023,
    title = {mkite: A distributed computing platform for high-throughput materials simulations},
    author = {Schwalbe-Koda, Daniel},
    year = {2023},
    journal = {arXiv:2301.08841},
    doi = {10.48550/arXiv.2301.08841},
    url = {https://doi.org/10.48550/arXiv.2301.08841},
    arxiv={2301.08841},
}
```

## License

The mkite suite is distributed under the following license: Apache 2.0 WITH LLVM exception.

All new contributions must be made under this license.

SPDX: Apache-2.0, LLVM-exception

LLNL-CODE-848161
loop-cgal 0.1.6
python bindings for cgal for implicit model meshing
Requires Python: >=3.7

# Loop-cgal

Loop-cgal is a Python package for mesh processing operations using the  CGAL (Computational Geometry Algorithms Library). It is designed for efficient geometric computations using pyvista objects.

## Features

- Python bindings for CGAL using `pybind11`.
- Current features:
    - clipping of 3D triangular surfaces
- Future features:
    - Marching cubes algorithm for isosurface extraction
    - Boolean operations on marching cube meshes.

## Installation

### Prerequisites

- C++17 or later
- Python 3.11 or later
- CGAL library
- Boost library
- CMake 3.15 or later
- pybind11
- scikit-build
- pyvista

### Build and Install

1. Clone the repository:
   ```bash
   git clone https://github.com/Loop3D/loop-cgal.git
   cd loop-cgal
   pip install .
   ```
2. Alternatively, you can install it directly from PyPI:
   ```bash
   pip install loop-cgal
   ```

### Windows using vcpkg

To install dependencies using [vcpkg](https://vcpkg.io/):

1. Install vcpkg following the [official guide](https://vcpkg.io/en/getting-started.html).
2. Install required libraries:
    ```bash
    vcpkg install yasm-tool:x86-windows cgal:x64-windows
    ```
3. Set the `VCPKG_ROOT` environment variable to point to your vcpkg installation directory.
4. Set the `CMAKE_ARGS` to include `-DCMAKE_TOOLCHAIN_FILE` and `-DVCPKG_ROOT` e.g. CMAKE_ARGS="-DCMAKE_TOOLCHAIN_FILE=$Env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake -DVCPKG_ROOT=$Env:VCPKG_ROOT"
4. Proceed with the build and installation steps as described above.
mkite-engines 25.11.3
engines for connecting messages to/from mkite
Requires Python: >=3.8
Keywords: workflow, materials-science

<div align="center">
  <img src="https://raw.githubusercontent.com/mkite-group/mkite_engines/main/docs/_static/mkite-engines.svg" width="400"><br>
</div>

# mkite_engines

mkite_engines implements interfaces that help distributing jobs to workers.
These interfaces are [producers and consumers](https://en.wikipedia.org/wiki/Producer%E2%80%93consumer_problem) of data, and are created to simplify data management and transfer between the production server and the task worker.

## Documentation

General tutorial for `mkite` and its plugins are available in the [main documentation](https://mkite.org).
Complete API documentation is pending.

## Installation

`mkite_engines` is essential to run all other components of mkite. To install this package, use pip:

```bash
pip install mkite_engines
```

Alternatively, for a development version, clone this repo and install it in editable form:

```bash
pip install -U git+https://github.com/mkite-group/mkite_engines
```

## Contributions

Contributions to the entire mkite suite are welcomed.
You can send a pull request or open an issue for this plugin or either of the packages in mkite.
When doing so, please adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) in the mkite suite.

The mkite package was created by Daniel Schwalbe-Koda <dskoda@ucla.edu>.

### Citing mkite

If you use mkite in a publication, please cite the following paper:

```bibtex
@article{mkite2023,
    title = {mkite: A distributed computing platform for high-throughput materials simulations},
    author = {Schwalbe-Koda, Daniel},
    year = {2023},
    journal = {arXiv:2301.08841},
    doi = {10.48550/arXiv.2301.08841},
    url = {https://doi.org/10.48550/arXiv.2301.08841},
    arxiv={2301.08841},
}
```

## License

The mkite suite is distributed under the following license: Apache 2.0 WITH LLVM exception.

All new contributions must be made under this license.

SPDX: Apache-2.0, LLVM-exception

LLNL-CODE-848161