dpg-tutorial-2025
Automated Workflows and Machine Learning for Materials Science Simulations
Science Score: 57.0%
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Repository
Automated Workflows and Machine Learning for Materials Science Simulations
Basic Info
- Host: GitHub
- Owner: pyiron-workshop
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: http://workshop.pyiron.org/DPG-tutorial-2025/
- Size: 21 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 2
Metadata Files
README.md
Hands-on Tutorial: Automated Workflows and Machine Learning for Materials Science Simulations
DPG Regensburg 2025
Abstract
Machine learning techniques in physics and materials science have revolutionized simulations and experimental analysis. Using these techniques to accurately predict, for example, material properties requires the manipulation and use of vast amounts of data. Manual processing and analysis quickly become impractical and error-prone, so the availability of automated workflows is critical to their efficient, reliable, and consistent application.
In this hands-on tutorial, we provide an interactive, practical introduction to workflow management using Pyiron (www.pyiron.org). Pyiron is an integrated materials science development environment based on Python and Jupyter notebooks that can be used for a wide range of simulation tasks, including rapid prototyping, coupling with experiments, and high-performance computing. The tutorial gives a general introduction to the use of Pyiron with a focus on atomistic simulation tasks. As a practical example, all steps of the workflow for the construction of ab initio phase diagrams will be performed interactively by all participants, e.g. the generation of DFT datasets, the training and validation of machine learning potentials as well as the construction of the phase diagram.
Cite this tutorial
Poul, M., Menon, S., Gaafer, H., Qamar, M., Drautz, R., Tilmann, H., & Neugebauer, J. (2025). DPG Spring Meeting 2025 Tutorial 'Automated Workflows and Machine Learning for Materials Science Simulations' (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.15065109
Owner
- Name: pyiron-workshop
- Login: pyiron-workshop
- Kind: organization
- Repositories: 1
- Profile: https://github.com/pyiron-workshop
Citation (CITATION.cff)
cff-version: 1.2.0 title: DPG Spring Meeting 2025 Tutorial 'Automated Workflows and Machine Learning for Materials Science Simulations' doi: 10.5281/zenodo.15065108 message: >- Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524 type: software authors: - given-names: Marvin family-names: Poul affiliation: Max Planck Institute for Sustainable Materials orcid: 'https://orcid.org/0000-0002-6029-8748' - given-names: Sarath family-names: Menon affiliation: Max Planck Institute for Sustainable Materials orcid: 'https://orcid.org/0000-0002-6776-1213' - given-names: Haitham family-names: Gaafer affiliation: Max Planck Institute for Sustainable Materials - given-names: Minaam family-names: Qamar orcid: 'https://orcid.org/0000-0002-3342-4307' affiliation: 'ICAMS, Ruhr-Universität Bochum' - given-names: Ralf family-names: Drautz orcid: 'https://orcid.org/0000-0001-7101-8804' affiliation: 'ICAMS, Ruhr-Universität Bochum' - given-names: Hickel family-names: Tilmann orcid: 'https://orcid.org/0000-0003-0698-4891' affiliation: 'Bundesanstalt für Materialforschung und -prüfung' - given-names: Jörg family-names: Neugebauer affiliation: Max Planck Institute for Sustainable Materials orcid: 'https://orcid.org/0000-0002-7903-2472' url: 'https://www.dpg-verhandlungen.de/year/2025/conference/regensburg/part/tut/session/5/contribution/1' license: "MIT" repository-code: https://github.com/pyiron-workshop/DPG-tutorial-2025 version: 0.8.13 abstract: >- Machine learning techniques in physics and materials science have revolutionized simulations and experimental analysis. Using these techniques to accurately predict, for example, material properties requires the manipulation and use of vast amounts of data. Manual processing and analysis quickly become impractical and error-prone, so the availability of automated workflows is critical to their efficient, reliable, and consistent application. In this hands-on tutorial, we provide an interactive, practical introduction to workflow management using Pyiron (www.pyiron.org). Pyiron is an integrated materials science development environment based on Python and Jupyter notebooks that can be used for a wide range of simulation tasks, including rapid prototyping, coupling with experiments, and high-performance computing. The tutorial gives a general introduction to the use of Pyiron with a focus on atomistic simulation tasks. As a practical example, all steps of the workflow for the construction of ab initio phase diagrams will be performed interactively by all participants, e.g. the generation of DFT datasets, the training and validation of machine learning potentials as well as the construction of the phase diagram.
GitHub Events
Total
- Create event: 9
- Release event: 2
- Issues event: 2
- Delete event: 5
- Public event: 1
- Push event: 29
- Pull request event: 15
Last Year
- Create event: 9
- Release event: 2
- Issues event: 2
- Delete event: 5
- Public event: 1
- Push event: 29
- Pull request event: 15