A bridge between trust and control: Computational workflows meet automated battery cycling

08 November 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.


battery assembly
battery cycling
data science
research data management
experimental workflows
instrument automation

Supplementary weblinks


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