JOM

, Volume 68, Issue 8, pp 2116–2125 | Cite as

Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

  • Jonathan Kenneth Bunn
  • Jianjun Hu
  • Jason R. Hattrick-Simpers
Article

Abstract

Manual attribution of crystallographic phases from high-throughput x-ray diffraction studies is an arduous task, and represents a rate-limiting step in high-throughput exploration of new materials. Here, we demonstrate a semi-supervised machine learning technique, SS-AutoPhase, which uses a two-step approach to identify automatically phases from diffraction data. First, clustering analysis is used to select a representative subset of samples automatically for human analysis. Second, an AdaBoost classifier uses the labeled samples to identify the presence of the different phases in diffraction data. SS-AutoPhase was used to identify the metallographic phases in 278 diffraction patterns from a FeGaPd composition spread sample. The accuracy of SS-AutoPhase was >82.6% for all phases when 15% of the diffraction patterns were used for training. The SS-AutoPhase predicted phase diagram showed excellent agreement with human expert analysis. Furthermore it was able to determine and identify correctly a previously unreported phase.

Supplementary material

11837_2016_2033_MOESM1_ESM.docx (397 kb)
Supplementary material 1 (DOCX 397 kb)

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Copyright information

© The Minerals, Metals & Materials Society 2016

Authors and Affiliations

  1. 1.SmartState Center for the Strategic Approaches to the Generation of Electricity, Department of Chemical EngineeringUniversity of South CarolinaColumbiaUSA
  2. 2.Department of Computer Science and EngineeringUniversity of South CarolinaColumbiaUSA

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