Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory

Abstract

The pressing need for improved energy materials calls for an acceleration of research to expedite their commercial application for the energy transition. To explore the vast amount of material candidates, developing high-throughput setups as well as enhancing knowledge production by synthesis of published data is essential. Therefore, managing data in a clearly defined structure in compliance with the FAIR principles is imperative. However, current data workflows from laboratory to publication often imply poorly structured data, limiting acceleration in materials research. Here, we demonstrate a comprehensive data management tool structuring data throughout its life-cycle from acquisition, analysis, and visualization to publication by means of an SQL database. This guarantees a persistent relation between experimental data, corresponding metadata, and analysis parameters. Manual interaction required to handle data is reduced to a minimum by integrating data acquisition devices (LabVIEW), defining script-based data analysis and curation routines, as well as procedurally uploading data upon publication (Python). Keeping the link, published data can be traced back to underlying experimental raw and metadata. While we highlight our developments for operando coupled electrochemical experiments, the used approach is generally applicable for other fields given its inherent customizability. Design of such automated data workflows is essential to develop efficient high-throughput setups. Further, they pave the way for self-driving labs and facilitate the applicability of machine learning methods.