Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

TL;DR

This paper introduces a machine learning-based data-driven method for analyzing synchrotron Laue X-ray microdiffraction scans, offering a faster alternative to traditional pattern indexing and enabling advanced pattern analysis.

Contribution

It presents a novel, mathematically formulated machine learning approach for analyzing diffraction scans, independent of conventional indexing, demonstrated on various material samples.

Findings

Effective analysis of diffraction patterns without indexing

Application to diverse material samples

Potential for faster, automated diffraction analysis

Abstract

We propose a novel data-driven approach for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. We demonstrate it through typical examples including polycrystalline BaTiO$_3$, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Lab. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern by pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning prospective for the development of suitable feature extraction, clustering and labeling algorithms.