Literature DB >> 26469294

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

A G Kusne1, D Keller, A Anderson, A Zaban, I Takeuchi.   

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.

Year:  2015        PMID: 26469294     DOI: 10.1088/0957-4484/26/44/444002

Source DB:  PubMed          Journal:  Nanotechnology        ISSN: 0957-4484            Impact factor:   3.874


  5 in total

1.  Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

Authors:  Rama K Vasudevan; Kamal Choudhary; Apurva Mehta; Ryan Smith; Gilad Kusne; Francesca Tavazza; Lukas Vlcek; Maxim Ziatdinov; Sergei V Kalinin; Jason Hattrick-Simpers
Journal:  MRS Commun       Date:  2019       Impact factor: 2.566

2.  A thermal-gradient approach to variable-temperature measurements resolved in space.

Authors:  Daniel O'Nolan; Guanglong Huang; Gabrielle E Kamm; Antonin Grenier; Chia-Hao Liu; Paul K Todd; Allison Wustrow; Gia Thinh Tran; David Montiel; James R Neilson; Simon J L Billinge; Peter J Chupas; Katsuyo S Thornton; Karena W Chapman
Journal:  J Appl Crystallogr       Date:  2020-04-23       Impact factor: 3.304

3.  Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach.

Authors:  Yuta Suzuki; Hideitsu Hino; Takafumi Hawai; Kotaro Saito; Masato Kotsugi; Kanta Ono
Journal:  Sci Rep       Date:  2020-12-11       Impact factor: 4.379

4.  Committee machine that votes for similarity between materials.

Authors:  Duong-Nguyen Nguyen; Tien-Lam Pham; Viet-Cuong Nguyen; Tuan-Dung Ho; Truyen Tran; Keisuke Takahashi; Hieu-Chi Dam
Journal:  IUCrJ       Date:  2018-10-30       Impact factor: 4.769

5.  Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning.

Authors:  Yuma Iwasaki; Masahiko Ishida; Masayuki Shirane
Journal:  Sci Technol Adv Mater       Date:  2019-12-20       Impact factor: 8.090

  5 in total

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