Spotlight: efficient automated global optimization in rietveld analysis of diffraction data.

Area of Science:

• Materials Science
• Crystallography
• Computational Chemistry

Background:

• Rietveld analysis of large powder diffraction datasets is computationally intensive and prone to failure in complex scenarios like high-temperature studies.
• Existing automated methods struggle with phase transformations and element repartitioning common in annealing experiments.
• Efficient data analysis is crucial for extracting meaningful insights from parametric and time-resolved diffraction studies.

Purpose of the Study:

• To develop a robust and efficient automated Rietveld analysis tool for powder diffraction data.
• To address the limitations of current automated methods in handling complex experimental conditions.
• To enable reliable and scalable analysis of large and challenging crystallographic datasets.

Main Methods:

• Developed Spotlight, a Python package integrating established Rietveld software (MAUD, GSAS, GSAS-II).
• Implemented global optimization using an ensemble of optimizers with hierarchical parallel execution on HPC clusters.
• Integrated automated machine learning for iterative design of refinement plans using a surrogate model.

Main Results:

• Spotlight successfully automates Rietveld analysis, overcoming bottlenecks in large-scale and complex experiments.
• Demonstrated efficacy on uranium molybdenum, Ti-6Al-4V, aluminium oxide, and lead sulphate datasets.
• The surrogate-based optimization approach ensures convergence and reliability of results.

Conclusions:

• Spotlight provides a reliable, efficient, and automated solution for complex Rietveld analysis.
• The package enhances the extraction of scientific insights from challenging powder diffraction experiments.
• Enables advanced materials characterization through scalable and automated crystallographic data processing.