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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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Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning

Yuta Suzuki^1,2, Hideitsu Hino³, Takafumi Hawai¹

¹Institute of Materials Structure Science, High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki, 305-0801, Japan.

Scientific Reports

|December 14, 2020

Summary

This summary is machine-generated.

A new machine learning (ML) model automates crystal system and space group classification from powder X-ray diffraction (XRD) patterns. This approach achieves high accuracy, streamlining materials science workflows.

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Area of Science:

Crystallography
Materials Science
Machine Learning

Background:

Crystal system and space group determination is crucial but time-consuming in materials science.
Manual analysis of powder X-ray diffraction (XRD) patterns presents a bottleneck in workflows.

Purpose of the Study:

To develop a machine learning (ML)-based approach for automated crystal system and space group classification.
To demonstrate the feasibility of using simulated XRD patterns for model training and validation.

Main Methods:

A tree-ensemble-based machine learning model was developed.
The model was trained and tested using simulated powder XRD patterns.

Main Results:

The ML model achieved over 90% accuracy for crystal system classification (excluding triclinic cases).
Space group classification accuracy reached 88% for five candidate groups.
An interpretable ML approach quantified expert knowledge and revealed potential for discovering new data characteristics.

Conclusions:

ML offers a powerful tool to automate and accelerate crystal structure analysis.
This data-driven method can enhance the efficiency and discover new insights in materials science.
Interpretable ML can bridge the gap between expert knowledge and data-driven discoveries.