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Related Experiment Video

Updated: Jun 4, 2025

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

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

Published on: June 8, 2016

End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction.

Qingsi Lai^1,2, Fanjie Xu^1,3, Lin Yao¹

¹DP Technology, Beijing, 100080, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|January 5, 2025

Summary

This summary is machine-generated.

XtalNet, a novel deep learning model, predicts crystal structures directly from Powder X-ray Diffraction (PXRD) data. This breakthrough automates a complex process, accelerating materials discovery.

Keywords:

crystal structure prediction deep learning equivariant deep generative model metal–organic frameworks (MOFs)powder X‐ray diffraction

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

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Published on: August 22, 2017

Area of Science:

Materials Science
Crystallography
Artificial Intelligence

Background:

Powder X-ray Diffraction (PXRD) is crucial for materials characterization but often requires extensive manual analysis.
Automated methods for PXRD analysis are typically coarse-grained, leaving fine-grained crystal structure prediction unaddressed.

Purpose of the Study:

To introduce XtalNet, the first equivariant deep generative model for end-to-end crystal structure prediction directly from PXRD data.
To overcome limitations of composition-only methods by incorporating PXRD as a conditional input.

Main Methods:

Developed XtalNet, an AI model comprising Contrastive PXRD-Crystal Pretraining (CPCP) and Conditional Crystal Structure Generation (CCSG) modules.
Leveraged PXRD patterns as a condition to guide the generation of crystal structures, enabling prediction of complex organic structures.

Main Results:

XtalNet achieved high accuracy in conditional crystal structure prediction on MOF datasets (hMOF-100 and hMOF-400).
Demonstrated a top-10 Match Rate of 90.2% for hMOF-100 and 79% for hMOF-400.
Successfully generated complex organic structures with up to 400 atoms in the unit cell.

Conclusions:

XtalNet enables direct, automated crystal structure prediction from experimental PXRD measurements.
Eliminates the need for manual intervention and reliance on external databases.
Accelerates the discovery of novel materials through automated crystal structure determination.