Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large...

Area of Science:

• Materials Science
• Crystallography
• Data Science

Background:

• Determining atomic crystal structures from experimental data, such as pair distribution function (PDF) analysis, is crucial for materials science.
• Traditional structure searching methods are often labor-intensive and time-consuming, hindering rapid material discovery and analysis.

Purpose of the Study:

• To develop a highly automated approach for identifying candidate atomic structures directly from pair distribution function (PDF) data.
• To streamline the structure refinement process, reducing the need for manual intervention.

Main Methods:

• The algorithm automatically queries web-based structural databases for potential structures matching experimental criteria.
• It performs automated structure refinements using both X-ray and neutron PDF data.
• The method is designed to be robust across diverse material types and experimental conditions.

Main Results:

• Demonstrated effectiveness and robustness in correctly identifying atomic crystal structures across various material systems.
• Successfully applied to crystalline and nanocrystalline materials, including complex oxides, nanoparticles, nanowires, low-symmetry, and magnetically ordered materials.
• Validated the algorithm's capability to handle locally distorted and doped structures.

Conclusions:

• The presented automated approach significantly reduces the effort required for traditional structure searching.
• This method paves the way for high-throughput, real-time automated analysis of PDF experiments.
• Enables faster and more efficient materials characterization and discovery.