Document Type
Letter to the Editor
Publication Date
7-29-2024
Abstract
There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed.
Keywords
artificial intelligence, autonomous research, data/database, X-ray diffraction (XRD), X-ray tomography
Language
English
Publication Title
MRS Communications
Grant
2117439
Rights
© The Author(s) 2024. This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ciardi, T.G., Nihar, A., Chawla, R. et al. Materials data science using CRADLE: A distributed, data-centric approach. MRS Communications 14, 601–611 (2024). https://doi.org/10.1557/s43579-024-00616-6
Manuscript Version
Final Publisher Version