Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X‑ray Diffraction.

Area of Science:

• Materials Science
• Crystallography
• Computational Chemistry

Background:

• Powder X-ray diffraction (XRD) is crucial for crystalline material characterization.
• Interpreting XRD patterns in multiphase systems is complex, manual, and requires expertise.
• Ambiguity in XRD data can lead to misinterpretation due to multiple possible phase solutions.

Purpose of the Study:

• To introduce Dara (data-driven automated Rietveld analysis), a framework to automate multiphase identification from XRD data.
• To enhance the robustness and accuracy of phase identification and refinement in complex XRD patterns.
• To reduce the manual effort and expertise required for XRD data analysis.

Main Methods:

• Dara framework utilizes an exhaustive tree search over plausible phase combinations within a chemical space.
• BGMN Rietveld refinement routine is employed to validate each hypothesized phase combination.
• Key features include structural database filtering, isostructural phase clustering, and peak-matching-based scoring.

Main Results:

• Dara automates the identification and refinement of multiple phases from powder XRD data.
• The framework enhances reliability and accuracy in phase identification, even with complex patterns.
• When ambiguity arises, Dara generates multiple hypotheses for expert or further tool-based decision.

Conclusions:

• Dara provides a scalable solution for analyzing complex XRD patterns, reducing human error and effort.
• The framework facilitates integration into multimodal characterization workflows.
• Dara represents a step towards automated materials discovery and characterization.