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

Powder X-ray diffraction (XRD) is a foundational technique
for
characterizing crystalline materials. However, the reliable interpretation
of XRD patterns, particularly in multiphase systems, remains a manual
and expertise-demanding task. As a characterization method that only
provides structural information, multiple reference phases can often
be fit to a single pattern, leading to potential misinterpretation
when alternative solutions are overlooked. To ease humans’
efforts and address the challenge, we introduce Dara (data-driven
automated Rietveld analysis), a framework designed to automate the
robust identification and refinement of multiple phases from powder
XRD data. Dara performs an exhaustive tree search over all plausible
phase combinations within a given chemical space and validates each
hypothesis using the BGMN Rietveld refinement routine. Key features
include structural database filtering, automatic clustering of isostructural
phases during tree expansion, and peak-matching-based scoring to identify
promising phases for refinement. When ambiguity exists, Dara generates
multiple hypothesis which can then be decided between by human experts
or with further characterization tools. By enhancing the reliability
and accuracy of phase identification, Dara enables scalable analysis
of realistic complex XRD patterns and provides a foundation for integration
into multimodal characterization workflows, moving toward fully self-driving
materials discovery.