View in EDS

Automation of Rietveld refinement through machine learning.

Saved in:
Bibliographic Details
Title: Automation of Rietveld refinement through machine learning.
Authors: Mun, Suk Jin1,2 (AUTHOR), Nam, Yoonsoo3 (AUTHOR), Choi, Sungkyun1,2 (AUTHOR) sungkyunchoi@skku.edu
Source: Journal of Applied Crystallography. Apr2026, Vol. 59 Issue 2, p564-577. 14p.
Subjects: Rietveld refinement, Machine learning, Materials science, Electronic data processing, Diffraction patterns, Convolutional neural networks, X-ray powder diffraction, Crystal structure
Abstract: Rietveld refinement is a widely used technique for determining crystal structures from powder X‐ray diffraction data. Despite its broad applicability, the refinement process often requires substantial manual effort and expert knowledge, which limits the pace of materials research. Here, we introduce a methodology based on convolutional neural networks that enables automated Rietveld refinement. A systematically generated training dataset, which incorporates diverse structural and profile parameters, allows the network to capture complex pattern–structure relationships effectively. A refined crystal structure is directly extracted from the experimental powder X‐ray diffraction pattern in a single inference step. The approach is validated using benchmark datasets of CeO2, Tb2BaCoO5 and PbSO4, achieving reliability factors comparable to those obtained from conventional methods. This work establishes a generalizable methodology by providing valuable insights into the development of autonomous diffraction analysis, with the potential to accelerate materials discovery and characterization. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Crystallography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Rietveld refinement is a widely used technique for determining crystal structures from powder X‐ray diffraction data. Despite its broad applicability, the refinement process often requires substantial manual effort and expert knowledge, which limits the pace of materials research. Here, we introduce a methodology based on convolutional neural networks that enables automated Rietveld refinement. A systematically generated training dataset, which incorporates diverse structural and profile parameters, allows the network to capture complex pattern–structure relationships effectively. A refined crystal structure is directly extracted from the experimental powder X‐ray diffraction pattern in a single inference step. The approach is validated using benchmark datasets of CeO2, Tb2BaCoO5 and PbSO4, achieving reliability factors comparable to those obtained from conventional methods. This work establishes a generalizable methodology by providing valuable insights into the development of autonomous diffraction analysis, with the potential to accelerate materials discovery and characterization. [ABSTRACT FROM AUTHOR]
ISSN:00218898
DOI:10.1107/S1600576726001494