Metadata-Version: 2.1
Name: aim2dat
Version: 0.2.0
Summary: Automated Ab-Initio Materials Modeling and Data Analysis Toolkit: Python library for pre-, post-processing and data management of ab-initio high-throughput workflows for computational materials science.
Home-page: https://github.com/aim2dat/aim2dat
Author-email: Holger-Dietrich Saßnick <holger-dietrich.sassnick@uni-oldenburg.de>, Timo Reents <timo.reents@uni-oldenburg.de>, Joshua Edzards <joshua.edzards@uni-oldenburg.de>
Maintainer-email: Holger-Dietrich Saßnick <holger-dietrich.sassnick@uni-oldenburg.de>
License: LGPL-2.1
Project-URL: Repository, https://github.com/aim2dat/aim2dat.git
Project-URL: Issues, https://github.com/aim2dat/aim2dat/issues
Project-URL: Changelog, https://github.com/aim2dat/aim2dat/blob/main/CHANGELOG
Keywords: ab-initio,dft,high-throughput,automated,materials-modeling,data-analysis,science,machine learning
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: AiiDA
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 (LGPLv2)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Description-Content-Type: text/markdown
License-File: LICENSE
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# aim2dat

aim2dat (Automated Ab-Initio Materials Modeling and Data Analysis Toolkit) is a library for pre-, post-processing and data management of ab-initio high-throughput workflows for computational materials science.
For further details and documentation, please visit https://aim2dat.github.io.

## Feature List

* Managing and analysing sets of crystals and molecules.
* Ab-initio high-throughput calculations based on [AiiDA](https://www.aiida.net).
* Plotting material's properties such as electronic band structures, projected density of states or phase diagrams.
* Interface to machine learning routines via [sci-kit learn](https://scikit-learn.org/stable/).
* Function analysis: discretizing and comparing 2-dimensional functions.
* Parsers for the DFT codes [CP2K](https://www.cp2k.org/about), [FHI-Aims](https://fhi-aims.org) and [QuantumESPRESSO](https://www.quantum-espresso.org) as well as [phonopy](https://phonopy.github.io/phonopy/) and [critic2](https://aoterodelaroza.github.io/critic2/).

## Installation

```sh
pip install aim2dat
```

More detailed instructions are given in the documentation (https://aim2dat.github.io/installation.html).

## Contributing

Contributions are very welcome and are directly handled via the code's [github repository](https://github.com/aim2dat/aim2dat).
Bug reports, feature requests or general discussions can be accomplished by filing an [issue](https://github.com/aim2dat/aim2dat/issues).
Extensions or changes to the code can also be directly suggested by opening a [pull request](https://github.com/aim2dat/aim2dat/pulls).
Some guidelines for code contributions are given in the documentation (https://aim2dat.github.io/#contributing).
