تقرير
An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations
| العنوان: | An Incremental Phase Mapping Approach for X-ray Diffraction Patterns using Binary Peak Representations |
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| المؤلفون: | Jha, Dipendra, Narayanachari, K. V. L. V., Zhang, Ruifeng, Liao, Justin, Keane, Denis T., Liao, Wei-keng, Choudhary, Alok, Chung, Yip-Wah, Bedzyk, Michael, Agrawal, Ankit |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted and presented at the International Workshop on Domain-Driven Data Mining (DDDM) as a part of the SIAM International Conference on Data Mining (SDM 2021). Contains 11 pages and 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.04011 |
| رقم الأكسشن: | edsarx.2211.04011 |
| قاعدة البيانات: | arXiv |
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