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

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.

Identifier
Source https://archive.materialscloud.org/record/2023.168
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1961
Provenance
Creator Kraus, Peter; Bainglass, Edan; Ramirez, Francisco F.; Svaluto-Ferro, Enea; Ercole, Loris; Kunz, Benjamin; Huber, Sebastiaan P.; Plainpan, Nukorn; Marzari, Nicola; Battaglia, Corsin; Pizzi, Giovanni
Publisher Materials Cloud
Publication Year 2023
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering