A Python workflow definition for computational materials design

doi:10.1039/D5DD00231A

A Python workflow definition for computational materials design

2025-10-10

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DOI：10.1039/D5DD00231A

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Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats; hindering interoperability and reproducibility of workflows in the field. To address this challenge; we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS; currently AiiDA; jobflow; and pyiron. This development is motivated by the similarity of these three Python-based WfMS; that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD; we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable; Accessible; Interoperable; Reusable (FAIR) workflows. To separate the scientific from the technical complexity; the PWD consists of three components: (1) a conda environment that specifies the software dependencies; (2) a Python module that contains the Python functions represented as nodes in the workflow graph; and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus; any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import; the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow; before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA; jobflow; and pyiron.

A Python workflow definition for computational materials design

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