Depending on what you want, there are several options to install and configure pyiron as seen blow. If you first want to quickly see how pyiron feels like, use the binder service on Mybinder.org (beta) (more info in Cloud Solutions), without the need for a local installation.
For a workstation installation with conda it boils down to
conda install -c conda-forge pyiron, see Installation on a workstation for more information and additional packages to be installed.
- Installation on a workstation
- Install pyiron on your cluster in 15 minutes!
- Advanced Configuration
- Detailed HPC Cluster Configuration
- Alternative Installation Options
- Demonstration and Training Environments
Finally once you have installed pyiron you can quickly test your installation with the following minimalistic example. Many more examples are available in the Github repository.
After the successful configuration you can start your first pyiron calculation. Navigate to the the projects directory and start a jupyter notebook or jupyter lab session correspondingly:
Open a new jupyter notebook and inside the notebook you can now validate your pyiron calculation by creating a test project, setting up an initial structure of bcc Fe, and visualising it using NGLview.
from pyiron import Project
pr = Project('test')
basis = pr.create_structure('Fe', 'bcc', 2.78)
Finally a first lammps calculation can be executed by:
ham = pr.create_job(pr.job_type.Lammps, 'lammpstestjob')
ham.structure = basis
ham.potential = ham.list_potentials()
To get a better overview of all the available functionality inside pyiron we recommend the examples provided in the examples section - Tutorials.