An extensible open-source solution for research digitalisation in materials science

Abstract Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialised systems for information and knowledge management have been created in materials science. However, the development and deployment of open adaptive systems for research support in materials science based on the acquisition, storage, and processing of different types of information remains unsolved. We propose MatInf —an extensible, open-source solution for research digitalisation in materials science based on an adaptive, flexible information management system for heterogeneous data sources. MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs. As an example, we demonstrate its application in high-throughput experimentation.

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Publication Details

Journal
npj Computational Materials
Published
2025-05-03
DOI
https://doi.org/10.1038/s41524-025-01618-1
Citations
6
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.11

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article

An extensible open-source solution for research digitalisation in materials science

Victor Dudarev, Lars Banko, Alfred Ludwig
6 citations
npj Computational Materials
Machine Learning in Materials Science
3.11
article

An extensible open-source solution for research digitalisation in materials science

Victor Dudarev, Lars Banko, Alfred Ludwig
article en
6 citations

Abstract

Abstract Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialised systems for information and knowledge management have been created in materials science. However, the development and deployment of open adaptive systems for research support in materials science based on the acquisition, storage, and processing of different types of information remains unsolved. We propose MatInf —an extensible, open-source solution for research digitalisation in materials science based on an adaptive, flexible information management system for heterogeneous data sources. MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs. As an example, we demonstrate its application in high-throughput experimentation.

npj Computational MaterialsVol. 11(1)
Ruhr University Bochum (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 8%
Machine Learning in Materials Science
3.11
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An extensible open-source solution for research digitalisation in materials science — Victor Dudarev, Lars Banko, et al. · npj Computational Materials (2025) | TGRS Research Map | TGRS