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 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.

• A set of representative provenance graphs, automatically generated using AiiDA • A set of supplementary screenshots of the Experiment component of the AiiDAlab-Aurora user interface   At the centre is a simple WorkChain node (orange), linked to a single CalcJob node (cf.Fig. S1).However, provided the Data nodes used to construct the WorkChain contain the relevant metadata information and instructions, AiiDA is able to process the out-of-band data into the same internal format as that used for in-band data.This widget allows the user to review the selected samples as well as protocols and their monitoring settings prior to submission via AiiDA.

Figure S1 :
Figure S1: An automatically generated provenance graph for an in-band battery cycling workflow.The central CyclingSequenceWorkChain node (orange) corresponds to the overall workflow.It is linked to three BatteryCyclerExperiment nodes (red), corresponding to the Protective charge, Formation cycles, and Long-term cycling protocols of the workflow.Note that the Exit Code for the Longterm cycling (upper BatteryCyclerExperiment node) is 150, denoting that the task was aborted via job monitoring; the other two BatteryCyclerExperiments have an Exit Code of 0, denoting successful completion.The green nodes correspond to the various Data nodes, required to assemble the CyclingSequenceWorkchain, and retrieve the raw data from the remote host running tomato.

Figure S2 :
Figure S2: An automatically generated provenance graph for an out-of-band battery cycling workflow.At the centre is a simple WorkChain node (orange), linked to a single CalcJob node (cf.Fig.S1).However, provided the Data nodes used to construct the WorkChain contain the relevant metadata information and instructions, AiiDA is able to process the out-of-band data into the same internal format as that used for in-band data.

Figure S4 :
Figure S4: The Select protocols section of the Experiment component of the AiiDA-Aurora user interface.This widget allows for selection of protocols from the Inventory.The protocols will be executed in the order as listed in the Selected section.

Figure S5 :
Figure S5: The Generate input section of the Experiment component of the AiiDA-Aurora user interface.This widget allows the user to review the selected samples as well as protocols and their monitoring settings prior to submission via AiiDA.

Figure
Figure S6: A comparison of the capacity degradation of all cells in the two studied cell batches.The colour of the swarm plots denotes batch of the cells.The data from the 230511 batch (blue) represents out-of-band data, as it has been imported into AiiDA from EC-Lab.The data from the 231012 batch (orange) has been gathered in-band, using an AiiDA workflow.The box plots show the statistics for all cells plotted.The behaviour of the cells cycled up to 4.2 V (upper left), 4.4 V (upper right), as well as 4.6 V (lower right) is consistent between the two batches.However, the cells cycled up to 4.8 V start at a vastly different capacity (∼175 mAh/g for the 231012 batch, vs ∼195 mAh/g for 230511), and their capacity degradation also seems to occur at different rates, with all cells in the 231012 batch (orange) stopped by the job monitor within 20 cycles.Note that for the batch 231012 (orange), only 25 out of the 32 assembled cells are shown, as 7 cells have failed before the first cycle, due to an assembly failure or a software error.