Peak Area Detection Network for Directly Learning Phase Regions from Raw X-ray Diffraction Patterns

DIpendra Jha, Aaron Gilad Kusne, Reda Al-Bahrani, Nam Nguyen, Wei Keng Liao, Alok Choudhary, Ankit Agrawal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

X-ray diffraction (XRD) is a well-known technique used by scientists and engineers to determine the atomic-scale structures as a basis for understanding the composition-structure-property relationship of materials. The current approach for the analysis of XRD data is a multi-stage process requiring several intensive computations such as integration along 2θ for conversion to 1D patterns (intensity-2θ), background removal by polynomial fitting, and indexing against a large database of reference peaks. It impacts the decisions about the subsequent experiments of the materials under investigation and delays the overall process. In this paper, we focus on eliminating such multi-stage XRD analysis by directly learning the phase regions from the raw (2D) XRD image. We introduce a peak area detection network (PADNet) that directly learns to predict the phase regions using the raw XRD patterns without any need for explicit preprocessing and background removal. PADNet contains specially designed large symmetrical convolutional filters at the first layer to capture the peaks and automatically remove the background by computing the difference in intensity counts across different symmetries. We evaluate PADNet using two sets of XRD patterns collected from SLAC and Bruker D-8 for the Sn-Ti-Zn-O composition space; each set contains 177 experimental XRD patterns with their phase regions. We find that PADNet can successfully classify the XRD patterns independent of the presence of background noise and perform better than the current approach of extrapolating phase region labels based on 1D XRD patterns.

Original languageEnglish (US)
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
StatePublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period7/14/197/19/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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