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

Area of Science:

• Materials Science
• Computational Science
• Data Science

Background:

• Effective research data management is crucial for data integrity and trust.
• Controlling the data gathering process is key to ensuring reliable scientific outcomes.
• Existing tools often lack seamless integration between experimental hardware and computational workflows.

Purpose of the Study:

• To develop and demonstrate tooling that bridges data management and experimental control.
• To enhance trust in scientific data through robust provenance tracking.
• To provide a comprehensive, open-source software solution for automated scientific workflows.

Main Methods:

• Interfacing off-the-shelf battery cycling hardware with the AiiDA computational workflow management system.
• Designing user interfaces for inventory, experiment design, and results analysis.
• Implementing features for workflow monitoring and importing external/legacy data.

Main Results:

• Successful integration of battery cycling hardware with AiiDA for automated experiments.
• Demonstrated ability to control experiments while ensuring data provenance.
• Development of user interfaces covering multiple stages of the research process.
• Provision of a complete, open-source software stack for the demonstrated workflow.

Conclusions:

• The developed tooling enhances research data management by ensuring data integrity and experimental control.
• The integrated system, particularly for automated battery cycling, fosters trust in scientific data.
• The open-source nature of the software stack promotes wider adoption and reproducibility in scientific research.