Abstract
AbstractIn view of the paradigm shift toward data-driven research in materials science and engineering, handling large amounts of data becomes increasingly important. The application of FAIR (findable, accessible, interoperable, reusable) data principles emphasizes the importance of metadata describing datasets. We propose a novel data processing and machine learning (ML) pipeline to extract metadata from micrograph image files, then combine image data and their metadata for microstructure classification with a deep learning approach compared to a classic ML approach. The ML model attained excellent performances with and without metadata and bears potential for performance improvement of further use cases within the community.
Graphical abstract
Funder
Deutsche Forschungsgemeinschaft
Universität des Saarlandes
Publisher
Springer Science and Business Media LLC
Reference48 articles.
1. C. Draxl, M. Scheffler, Big data-driven materials science and its FAIR data infrastructure, in Handbook of Materials Modeling. ed. by W. Andreoni, S. Yip (Springer International Publishing, Cham, 2020)
2. L. Himanen, A. Geurts, A.S. Foster, P. Rinke, Data-driven materials science: status, challenges, and perspectives. Adv. Sci. 6(21), 1900808 (2019)
3. M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak et al., The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3(1), 160018 (2016)
4. M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.J. Bungartz, C. Felser et al., FAIR data enabling new horizons for materials research. Nature 604(7907), 635–642 (2022)
5. F.X. Coudert, Materials databases: the need for open, interoperable databases with standardized data and rich metadata. Adv. Theory Simul. 2(11), 1900131 (2019)
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