2025/05/19 by Yannick Kuhn, Kuhn, Yannick, Rana, Bhawna +16
Engineering · Materials Science · #Advanced Memory and Neural Computing #Data Analysis #Databases (cs.DB) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning in Materials Science #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2505.13566
openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Interdisciplinary collaboration in battery science is required for rapid evaluation of better compositions and materials. However, diverging domain vocabulary and non-compatible experimental results slow down cooperation. We critically assess the current state-of-the-art and develop a structured data management and interpretation system to make data curation sustainable. The techniques we utilize comprise ontologies to give a structure to knowledge, database systems tenable to the FAIR principles, and software engineering to break down data processing into verifiable steps. To demonstrate our approach, we study the applicability of the Galvanostatic Intermittent Titration Technique on various electrodes. Our work is a building block in making automated material science scale beyond individual laboratories to a worldwide connected search for better battery materials.