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Updated: Aug 16, 2025

$X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects$

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Published on: June 8, 2016

A noise-robust data assimilation method for crystal structure determination using powder diffraction intensity.

Seiji Yoshikawa¹, Ryuhei Sato¹, Ryosuke Akashi¹

¹Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.

The Journal of Chemical Physics

|December 22, 2022

Summary

This study enhances crystal structure prediction by improving how experimental X-ray diffraction (XRD) data is used in simulations. A new correlation-coefficient penalty function makes the method more robust to noisy XRD data.

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Last Updated: Aug 16, 2025

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Published on: April 24, 2018

Area of Science:

Condensed-matter science
Crystallography
Materials science

Background:

Crystal structure prediction is a significant challenge in materials science.
Experimental powder X-ray diffraction (XRD) data can aid crystal structure searches.
Previous methods used a crystallinity-type penalty function for data assimilation.

Purpose of the Study:

To improve the success rate and noise robustness of crystal structure prediction using XRD data.
To introduce a new correlation-coefficient-type penalty function for data assimilation.
To evaluate the effectiveness of the new penalty function on SiO2 coesite and ɛ-Zn(OH)2.

Main Methods:

Simulated annealing for crystal structure search.
Data assimilation of experimental powder XRD data into simulations.
Development and application of a correlation-coefficient-type penalty function.

Main Results:

The new penalty function demonstrates adaptability to XRD data with experimental noise.
Improved success rate and robustness in crystal structure prediction.
Successful application to SiO2 coesite and ɛ-Zn(OH)2.

Conclusions:

The correlation-coefficient penalty function enhances XRD data assimilation for crystal structure prediction.
This method offers improved accuracy and reliability, especially with noisy experimental data.
The approach is effective for complex materials like SiO2 coesite and ɛ-Zn(OH)2.