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

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.

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Acknowledgements

The work is funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award DE-AR0000492. We would like to acknowledge the support of the South Carolina SmartState Center for Strategic Approaches to the Generation of Electricity (SAGE).

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Correspondence to Jason R. Hattrick-Simpers.

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Bunn, J.K., Hu, J. & Hattrick-Simpers, J.R. Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns. JOM 68, 2116–2125 (2016). https://doi.org/10.1007/s11837-016-2033-8

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Keywords

  • Hierarchal Cluster Analysis
  • Human Expert
  • Dynamic Time Warping
  • Fe3Si
  • Training Sample Size