Parallax in angular sensitive powder diffraction tomography
While a few methods for the determination of depth-resolved strain distributions each with inherent limitations are available, tomographic reconstruction has been applied to this problem in only a limited sense. One of the challenges was the potential impact of geometric parallax, which constitutes a non-negligible lateral offset of diffraction information arising from different sample depths at the detector. Here, the effect of parallax was investigated and two main results have emerged. First, the impact of parallax was found to be additive to other offset contributions, which implies a straightforward correction. Second, for tomographic scans utilizing a full 360{\deg} rotation parallax has been found to have no impact on reconstructions of angular information.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
opXRD: Open Experimental Powder X-ray Diffraction Database
Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automa…
Transfer LearningdeCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models
Novel materials drive progress across applications from energy storage to electronics. Automated characterization of material structures with machine learning methods offers a promising strategy for accelerating this key…
Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra
Powder X-ray diffraction analysis is a critical component of materials characterization methodologies. Discerning characteristic Bragg intensity peaks and assigning them to known crystalline phases is the first qualitati…
Data AugmentationArtifact Identification in X-ray Diffraction Data using Machine Learning Methods
The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in…
BIG-bench Machine LearningAb Initio Structure Solutions from Nanocrystalline Powder Diffraction Data
A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is…