Literature DB >> 19895071

Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization.

C J Long1, D Bunker, X Li, V L Karen, I Takeuchi.   

Abstract

In this work we apply a technique called non-negative matrix factorization (NMF) to the problem of analyzing hundreds of x-ray microdiffraction (microXRD) patterns from a combinatorial materials library. An in-house scanning x-ray microdiffractometer is used to obtain microXRD patterns from 273 different compositions on a single composition spread library. NMF is then used to identify the unique microXRD patterns present in the system and quantify the contribution of each of these basis patterns to each experimental diffraction pattern. As a baseline, the results of NMF are compared to the results obtained using principle component analysis. The basis patterns found using NMF are then compared to reference patterns from a database of known structural patterns in order to identify known structures. As an example system, we explore a region of the Fe-Ga-Pd ternary system. The use of NMF in this case reduces the arduous task of analyzing hundreds of microXRD patterns to the much smaller task of identifying only nine microXRD patterns.

Year:  2009        PMID: 19895071     DOI: 10.1063/1.3216809

Source DB:  PubMed          Journal:  Rev Sci Instrum        ISSN: 0034-6748            Impact factor:   1.523


  9 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.  Evaluation of the Current Status of the Combinatorial Approach for the Study of Phase Diagrams.

Authors:  W Wong-Ng
Journal:  J Res Natl Inst Stand Technol       Date:  2012-12-21

4.  Enhancing deep-learning training for phase identification in powder X-ray diffractograms.

Authors:  Jan Schuetzke; Alexander Benedix; Ralf Mikut; Markus Reischl
Journal:  IUCrJ       Date:  2021-04-01       Impact factor: 4.769

5.  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

6.  Automated prediction of lattice parameters from X-ray powder diffraction patterns.

Authors:  Sathya R Chitturi; Daniel Ratner; Richard C Walroth; Vivek Thampy; Evan J Reed; Mike Dunne; Christopher J Tassone; Kevin H Stone
Journal:  J Appl Crystallogr       Date:  2021-11-30       Impact factor: 3.304

7.  Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction.

Authors:  Jordan Venderley; Krishnanand Mallayya; Michael Matty; Matthew Krogstad; Jacob Ruff; Geoff Pleiss; Varsha Kishore; David Mandrus; Daniel Phelan; Lekhanath Poudel; Andrew Gordon Wilson; Kilian Weinberger; Puspa Upreti; Michael Norman; Stephan Rosenkranz; Raymond Osborn; Eun-Ah Kim
Journal:  Proc Natl Acad Sci U S A       Date:  2022-06-09       Impact factor: 12.779

8.  On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets.

Authors:  Aaron Gilad Kusne; Tieren Gao; Apurva Mehta; Liqin Ke; Manh Cuong Nguyen; Kai-Ming Ho; Vladimir Antropov; Cai-Zhuang Wang; Matthew J Kramer; Christian Long; Ichiro Takeuchi
Journal:  Sci Rep       Date:  2014-09-15       Impact factor: 4.379

Review 9.  Progress and prospects for accelerating materials science with automated and autonomous workflows.

Authors:  Helge S Stein; John M Gregoire
Journal:  Chem Sci       Date:  2019-09-20       Impact factor: 9.825

  9 in total

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