ml4ms-workshop
Machine Learning Modalities for Materials Science
Science Score: 44.0%
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○Scientific vocabulary similarity
Low similarity (1.7%) to scientific vocabulary
Last synced: 10 months ago
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Repository
Machine Learning Modalities for Materials Science
Basic Info
- Host: GitHub
- Owner: pyiron-workshop
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: http://workshop.pyiron.org/ML4MS-workshop/
- Size: 15.3 MB
Statistics
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 1
Created about 2 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
Citation
Owner
- Name: pyiron-workshop
- Login: pyiron-workshop
- Kind: organization
- Repositories: 1
- Profile: https://github.com/pyiron-workshop
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Machine Learning Modalities for Materials Science
workshop: Maximizing High-Throughput Discovery and Machine
Learning Efficiency Through Computational Workflows
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: Sarath
family-names: Menon
email: s.menon@mpie.de
affiliation: Max Planck Institute for Sustainable Materials
orcid: 'https://orcid.org/0000-0002-6776-1213'
- given-names: Jörg
family-names: Neugebauer
email: j.neugebauer@mpie.de
affiliation: Max Planck Institute for Sustainable Materials
orcid: 'https://orcid.org/0000-0002-7903-2472'
repository-code: 'https://github.com/pyiron-workshop/ML4MS-workshop'
url: 'https://ml4ms.ijs.si/speakers/joerg-neugebauer/'
abstract: "The advent of high-throughput computation and discovery combined with machine learning is revolutionizing the field of computational materials science. It enables the simulation of large systems and complex material properties with ab initio accuracy. However, the development of these data-driven activities is often a computationally complex and intensive task, requiring the combination and orchestration of multiple and often incompatible simulation codes. Automated, reliable, and robust computational workflows are required to design and execute the underlying complex simulation protocols. Using the pyiron framework (pyiron.org), the tutorial provides a hands-on introduction to all aspects of workflow design, testing, and execution, with a strong focus on materials science and atomistic simulations. \uFEFF"
license: GPL-3.0-or-later
GitHub Events
Total
- Push event: 2
- Pull request event: 1
Last Year
- Push event: 2
- Pull request event: 1
Dependencies
binder/environment.yml
pypi
- pyxtal *
- tensorflow *