Dara: Automated multiple-hypothesis phase identification and refinement from powder X-ray diffraction

TL;DR

Dara automates the identification and refinement of multiple phases in powder X-ray diffraction data, reducing manual effort and improving accuracy in complex material characterization.

Contribution

It introduces a novel framework that exhaustively searches and validates phase hypotheses using Rietveld refinement, enhancing reliability in multiphase XRD analysis.

Findings

Automates phase identification with exhaustive tree search.

Validates hypotheses using robust Rietveld refinement.

Generates multiple plausible phase hypotheses for expert review.

Abstract

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 a robust Rietveld refinement routine (BGMN). Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, 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 characteriztion 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.