Inferring phase diagrams from X-ray data with background signals using graph segmentation

Abstract

Automated composition-structure-processing phase diagram creation is critical for high-throughput experimental material studies. In particular, diffractogram datasets with large background signals are especially difficult to identify the phase regions. In this work, we proposed a novel graph segmentation algorithm from computer vision to solve the phase diagram prediction problem from X-ray diffraction data with large background signals. We introduced a novel background subtraction algorithm with graph-based clustering/segmentation to build the BGPhase algorithm. Experiments on three datasets with the Al–Cu–Mo material family showed that our phase attribution algorithm can achieve high prediction accuracy ranging from 88.6 to 94.8% or with MCC scores ranging from 0.715 to 0.890. The algorithm can be accessed online at http://mleg.cse.sc.edu/bgphase.