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A new Python Workflow Definition (PWD) format enhances interoperability and reproducibility for computational materials science workflows. This standardized exchange format allows seamless sharing between AiiDA, jobflow, and pyiron, promoting FAIR data principles.

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Area of Science:

  • Computational Materials Science
  • Scientific Workflow Management
  • Software Interoperability

Background:

  • Diverse workflow formats in computational materials science hinder interoperability and reproducibility.
  • Existing Python-based Workflow Management Systems (WfMS) like AiiDA, jobflow, and pyiron share structural similarities in representing workflows as graphs.
  • The need for a standardized exchange format to facilitate seamless workflow sharing and execution across different WfMS is critical.

Purpose of the Study:

  • To introduce the Python Workflow Definition (PWD) as a novel workflow exchange format.
  • To foster interoperability and reproducibility among Python-based WfMS (AiiDA, jobflow, pyiron).
  • To promote the principles of Findable, Accessible, Interoperable, Reusable (FAIR) workflows in computational materials science.

Main Methods:

  • Developed the Python Workflow Definition (PWD) comprising three components: conda environment for dependencies, Python module for workflow functions, and a JSON-formatted workflow graph.
  • Implemented support for Directed Acyclic Graph (DAG)-based workflows in the initial PWD version.
  • Created a PWD Python library enabling export and import of workflows between AiiDA, jobflow, and pyiron.

Main Results:

  • Successfully demonstrated the export and import of DAG-based workflows between AiiDA, jobflow, and pyiron using the PWD format.
  • The PWD facilitates the adjustment of input parameters and assignment of computing resources post-import.
  • The PWD Python library effectively bridges the gap between the specified WfMS for workflow exchange.

Conclusions:

  • The Python Workflow Definition (PWD) provides a standardized solution for sharing workflows across different Python-based WfMS.
  • PWD significantly enhances workflow interoperability and reproducibility in computational materials science, aligning with FAIR data principles.
  • The PWD framework, with its modular design and library support, is poised to streamline computational materials science research by simplifying workflow management and execution.