Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

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



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


X-ray diffraction (XRD) is a well-known technique used by scientists and engineers to determine the atomic-scale structures to understand 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 2 for conversion to 1D patterns (intensity-2), 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.
Conference Dates
July 14-19, 2019
Conference Location
Budapest, HU
Conference Title
International Joint Conference on Neural Networks


Jha, D. , Kusne, A. , Agrawal, A. , Choudhary, A. , Al-Bahrani, R. , Nguyen, N. and Liao, W. (2019), Peak Area Detection Network for Directly Learning Phase Regions from Raw X-ray Diffraction Patterns, International Joint Conference on Neural Networks, Budapest, HU, [online], (Accessed May 22, 2024)


If you have any questions about this publication or are having problems accessing it, please contact

Created July 14, 2019, Updated October 14, 2021