Authors:
Dipendra Jha
1
;
K. V. L. V. Narayanachari
2
;
Ruifeng Zhang
2
;
Denis T. Keane
3
;
Wei-keng Liao
1
;
Alok Choudhary
1
;
Yip-Wah Chung
2
;
Michael J. Bedzyk
2
and
Ankit Agrawal
1
Affiliations:
1
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, U.S.A.
;
2
Department of Materials Science and Engineering, Northwestern University, Evanston, IL, U.S.A.
;
3
DND-CAT Synchrotron Research Center, Northwestern University, Evanston, IL, U.S.A.
Keyword(s):
X-ray Diffraction, Phase Clustering, Unsupervised Learning, Fuzzy C-means Clustering, Hierarchical Clustering, Composition-phase Diagram, Fuzzy Representation.
Abstract:
X-ray diffraction (XRD) is a widely used experiment in materials science to understand the composition-structure-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 init
ial 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.
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