An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations

by   Dipendra Jha, et al.

Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become impossible for domain scientists to process them manually. Recently, they have started leveraging standard clustering techniques, to reduce the XRD pattern representations requiring manual efforts for labeling and verification. Nevertheless, these standard clustering techniques do not handle problem-specific aspects such as peak shifting, adjacent peaks, background noise, and mixed phases; hence, resulting in incorrect composition-phase diagrams that complicate further steps. Here, we leverage data mining techniques along with domain expertise to handle these issues. In this paper, we introduce an incremental phase mapping approach based on binary peak representations using a new threshold based fuzzy dissimilarity measure. The proposed approach first applies an incremental phase computation algorithm on discrete binary peak representation of XRD samples, followed by hierarchical clustering or manual merging of similar pure phases to obtain the final composition-phase diagram. We evaluate our method on the composition space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are verified by domain scientists and closely resembles the manually computed ground-truth composition-phase diagrams. The proposed approach takes us closer towards achieving the goal of complete end-to-end automated XRD analysis.


page 7

page 9


Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering

Analyzing large X-ray diffraction (XRD) datasets is a key step in high-t...

Distributed Clustering Algorithm for Spatial Data Mining

Distributed data mining techniques and mainly distributed clustering are...

Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery

High-Throughput materials discovery involves the rapid synthesis, measur...

A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering

Persistence diagrams have been widely used to quantify the underlying fe...

Bayesian inference of composition-dependent phase diagrams

Phase diagrams serve as a highly informative tool for materials design, ...

Multi-objective Clustering Algorithm with Parallel Games

Data mining and knowledge discovery are two important growing research f...

Please sign up or login with your details

Forgot password? Click here to reset