Materials Data Science Using CRADLE: A Distributed, Data-Centric Approach

Thomas G. Ciardi, Case Western Reserve University
Arafath Nihar, Case Western Reserve University
Rounak Chawla, Case Western Reserve University
Olatunde Akanbi, Case Western Reserve University
Pawan K. Tripathi, Case Western Reserve University
Yinghui Wu, Case Western Reserve University
Vipin Chaudhary, Case Western Reserve University
Roger H. French, Case Western Reserve University

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: (Figure presented.)