Classification and statistical analysis of structural disorder in crystalline materials

Approximately 50% of entries in the Inorganic Crystal Structure Database (ICSD; https://www.fiz-karlsruhe.de/en) exhibit some form of structural disorder. This work aims to provide a thorough analysis of structurally disordered materials within the ICSD, using data extracted from crystallographic information files. To achieve this, we derive a classification of structurally disordered crystalline materials described by their spatially averaged structures and introduce a range of quantitative measures of structural disorder. The overarching aim of this classification and analysis is to facilitate high-throughput and machine learning studies of disordered materials. To demonstrate the application of our approach, we perform statistical analysis of the disordered compounds reported in the ICSD to identify general trends in the distribution of disorder across different chemical elements, structures and classes of materials.

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Publication Details

Journal
Journal of Applied Crystallography
Published
2025-05-08
DOI
https://doi.org/10.1107/s1600576725003000
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.55

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article

Classification and statistical analysis of structural disorder in crystalline materials

C. Matilda Collins, Matthew J. Rosseinsky, Dmytro Antypov, John B. Claridge et al.
3 citations
Journal of Applied Crystallography
Machine Learning in Materials Science
1.55
article

Classification and statistical analysis of structural disorder in crystalline materials

C. Matilda Collins, Matthew J. Rosseinsky, Dmytro Antypov, John B. Claridge, Matthew S. Dyer
article en
3 citations

Abstract

Approximately 50% of entries in the Inorganic Crystal Structure Database (ICSD; https://www.fiz-karlsruhe.de/en) exhibit some form of structural disorder. This work aims to provide a thorough analysis of structurally disordered materials within the ICSD, using data extracted from crystallographic information files. To achieve this, we derive a classification of structurally disordered crystalline materials described by their spatially averaged structures and introduce a range of quantitative measures of structural disorder. The overarching aim of this classification and analysis is to facilitate high-throughput and machine learning studies of disordered materials. To demonstrate the application of our approach, we perform statistical analysis of the disordered compounds reported in the ICSD to identify general trends in the distribution of disorder across different chemical elements, structures and classes of materials.

Journal of Applied CrystallographyVol. 58(3)
University of Liverpool (GB), Leverhulme Trust (GB)
Leverhulme Trust
Openalex Percentile: Top 18%
Machine Learning in Materials Science
1.55
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Classification and statistical analysis of structural disorder in crystalline materials — C. Matilda Collins, Matthew J. Rosseinsky, et al. · Journal of Applied Crystallography (2025) | TGRS Research Map | TGRS