AI Article Synopsis

  • Advances in materials fabrication and characterization have led to an increase in experimental data, necessitating high-throughput data analysis techniques to understand material relationships.
  • The GRENDEL algorithm is proposed as an effective method for analyzing structural data from combinatorial libraries to identify phase diagrams and phases using x-ray diffraction and Raman spectroscopy.
  • GRENDEL optimizes results with physical constraints and allows for the integration of external data sources, while the Sunburst radial tree map visually represents material structure-property relationships from the analysis.

Article Abstract

Advances in high-throughput materials fabrication and characterization techniques have resulted in faster rates of data collection and rapidly growing volumes of experimental data. To convert this mass of information into actionable knowledge of material process-structure-property relationships requires high-throughput data analysis techniques. This work explores the use of the Graph-based endmember extraction and labeling (GRENDEL) algorithm as a high-throughput method for analyzing structural data from combinatorial libraries, specifically, to determine phase diagrams and constituent phases from both x-ray diffraction and Raman spectral data. The GRENDEL algorithm utilizes a set of physical constraints to optimize results and provides a framework by which additional physics-based constraints can be easily incorporated. GRENDEL also permits the integration of database data as shown by the use of critically evaluated data from the Inorganic Crystal Structure Database in the x-ray diffraction data analysis. Also the Sunburst radial tree map is demonstrated as a tool to visualize material structure-property relationships found through graph based analysis.

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Source
http://dx.doi.org/10.1088/0957-4484/26/44/444002DOI Listing

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