Enhancing Phase Mapping for High-throughput X-ray Diffraction Experiments using Fuzzy Clustering

X-ray diffraction (XRD) is a widely used experiment in materials science to understand the compositionstructure-property relationships of materials for designing and discovering new materials. A key aspect of XRD analysis is that the composition-phase diagram is composed of not only pure phases but also their mixed phases. Hard clustering approach treats the mixed phases as separate independent clusters from their constituent pure phases, hence, resulting in incorrect phase diagrams which complicate the next steps. Here, we present a novel clustering approach of XRD patterns by leveraging a fuzzy clustering technique that can significantly enhance the potential phase mapping and reduce the manual efforts involved in XRD analysis. The proposed approach first generates an initial composition-phase diagram and initial pure phase representations by applying the fuzzy c-means clustering algorithm, followed by hierarchical clustering to accomplish effortless manual merging of similar initial pure phases to generate the final composition-phase diagram. The proposed method is evaluated on the XRD samples from two high-throughput composition-spread experiments of Co-Ni-Ta and Co-Ti-Ta ternary alloy systems. Our results demonstrate significant improvement compared to hard clustering and almost completely eliminate manual efforts.