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High-throughput determination of structural phase diagram and constituent phases using GRENDEL



Aaron G. Kusne


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.


machine learning, Materials informatics, MGI, high throughput phase diagram


Kusne, A. (2015), High-throughput determination of structural phase diagram and constituent phases using GRENDEL, Nanotechnology, [online], (Accessed April 19, 2024)
Created October 15, 2015, Updated June 3, 2017