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.