Accelerating battery research with an interoperable interface between FINALES and Kadi4Mat
Abstract
This study investigates how automated and interoperable research infrastructures can accelerate experimental materials research, using battery formation as a case study. We introduce a methodological framework that integrates the FINALES and Kadi Research Data Management ecosystems, enabling coordinated experiment execution, data management, and analysis across distributed research infrastructures. The FINALES framework orchestrates experiment planning and execution on the POLiS Materials Acceleration Platform, while Kadi handles data management, visualization, experiment selection, and persistent storage. This interoperability enables reproducible, coordinated, end-to-end experimental workflows that connect automated systems and human-operated processes across multiple research sites. To showcase the framework's capabilities, we investigate the formation process of sodium-ion coin cells, a critical step that influences cell lifetime. Using a dataset generated through the proposed infrastructure, we analyze the relationships between formation parameters and electrochemical performance. A Gaussian process model is employed to identify regions associated with favorable performance within the investigated parameter domain while quantifying predictive uncertainty, highlighting where additional experimental evidence would be most informative. The presented workflow demonstrates that interoperable, automated infrastructures can support experimental campaigns, enhance data consistency and traceability, and support reproducible, data-driven analysis. While demonstrated through a battery application, the framework provides a general methodology for integrating distributed experimental platforms with RDM systems, transferable across materials science and engineering domains.
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BibTeX
@misc{tosato2026accelerating,
title = {{Accelerating battery research with an interoperable interface between FINALES and Kadi4Mat}},
author = {Giovanna Tosato and Leon Merker and Monika Vogler and Michael Selzer and Arnd Koeppe},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.00909},
eprint = {2605.00909},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.00909},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Accelerating battery research with an interoperable interface between FINALES and Kadi4Mat
AU - Giovanna Tosato
AU - Leon Merker
AU - Monika Vogler
AU - Michael Selzer
AU - Arnd Koeppe
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2605.00909
UR - https://arxiv.org/abs/2605.00909
AB - This study investigates how automated and interoperable research infrastructures can accelerate experimental materials research, using battery formation as a case study. We introduce a methodological framework that integrates the FINALES and Kadi Research Data Management ecosystems, enabling coordinated experiment execution, data management, and analysis across distributed research infrastructures. The FINALES framework orchestrates experiment planning and execution on the POLiS Materials Acceleration Platform, while Kadi handles data management, visualization, experiment selection, and persistent storage. This interoperability enables reproducible, coordinated, end-to-end experimental workflows that connect automated systems and human-operated processes across multiple research sites. To showcase the framework's capabilities, we investigate the formation process of sodium-ion coin cells, a critical step that influences cell lifetime. Using a dataset generated through the proposed infrastructure, we analyze the relationships between formation parameters and electrochemical performance. A Gaussian process model is employed to identify regions associated with favorable performance within the investigated parameter domain while quantifying predictive uncertainty, highlighting where additional experimental evidence would be most informative. The presented workflow demonstrates that interoperable, automated infrastructures can support experimental campaigns, enhance data consistency and traceability, and support reproducible, data-driven analysis. While demonstrated through a battery application, the framework provides a general methodology for integrating distributed experimental platforms with RDM systems, transferable across materials science and engineering domains.
ER -