Materials data science using CRADLE: A distributed, data-centric approach

作者：Thomas G. Ciardi, Arafath Nihar, Rounak Chawla, Olatunde D. Akanbi, Pawan Kumar Tripathi, Yinghui Wu, Vipin Chaudhary, Roger H. French · 发表于：MRS Communications · 年份：2024 · DOI：10.1557/s43579-024-00616-6 · 被引用次数：12 · 研究领域：Machine Learning in Materials Science、Advanced X-ray and CT Imaging、Scientific Computing and Data Management

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. Graphical abstract