CITATION.cff

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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