.. _about:
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About
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Introduction
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:alt: Screenshot of pyiron running inside jupyterlab.
pyiron is an integrated development environment for implementing, testing, and running simulations in computational materials science. It combines several tools in a common platform:
• Atomic structure objects – compatible to the `Atomic Simulation Environment (ASE) `_.
• Atomistic simulation codes – like `LAMMPS `_ and `VASP `_.
• Feedback Loops – to construct dynamic simulation life cycles.
• Hierarchical data management – interfacing with storage resources like SQL and `HDF5 `_.
• Integrated visualization – based on `NGLview `_.
• Interactive simulation protocols - based on `Jupyter notebooks `_.
• Object oriented job management – for scaling complex simulation protocols from single jobs to high-throughput simulations.
pyiron (called pyron) is developed in the `Computational Materials Design department `_ of `Joerg Neugebauer `_ at the `Max Planck Institut für Eisenforschung (Max Planck Institute for iron research) `_. While its original focus was to provide a framework to develop and run complex simulation protocols as needed for ab initio thermodynamics it quickly evolved into a versatile tool to manage a wide variety of simulation tasks. In 2016 the `Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) `_ joined the development of the framework with a specific focus on high throughput applications. In 2018 pyiron was released as open-source project.
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Getting Help
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Technical issues and bugs should be reported on `Github `_ all other questions can be asked on `stackoverflow using the tag pyiron `_.
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Release history
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Release 0.2.0 (2018)
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* Implement interactive interface to communicate with codes at runtime.
Release 0.1.0 (2018)
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* opensource release - licensed under the BSD license.
* installation available on pip and anaconda.
* moved opensource repository to github.
Release 0.0.9 (2017)
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* Name changed from PyIron to pyiron
* Fileoperations implemented (move, copy_to and remove).
* NGLview for visualisation.
* Atoms class speedup.
* Serial- and parallelmaster work with the cluster environment.
* Python 3.6 support added.
Release 0.0.8 (2016)
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* Rewirte serial- and parallelmaster.
* Deprecated Qt environment in favor of jupyter.
* Python 3.5 support added.
* Use anaconda as recommended Python environment.
* Switch to Gitlab rather than subversion.
Release 0.0.5 (2015)
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* Linux and Mac OS X support added.
* ASE compatible atom and atoms class.
Release 0.0.1 (2011)
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* initial version named PyCMW