Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning

TL;DR

RADAR-PD is a novel machine learning framework that automates phase identification and quantification in powder diffraction, working effectively with both X-ray and neutron data for autonomous materials discovery.

Contribution

Introduces RADAR-PD, a mismatch-tolerant neural network coupled with physics-based verification for automated, multiphase powder diffraction analysis across different instruments.

Findings

RADAR-PD outperforms DARA in phase recovery on PXRD benchmarks.

It provides robust analysis for complex neutron datasets.

Addresses unmet needs in automated neutron diffraction analysis.

Abstract

Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Existing workflows rely heavily on search--match heuristics and manual Rietveld refinement, and broadly usable end-to-end automation is especially limited for neutron powder diffraction, where comparable tools are largely absent. Here we introduce RADAR-PD, a modality-aware machine learning framework for phase identification and quantification across both X-ray and neutron powder diffraction. RADAR-PD couples a mismatch-tolerant neural network operating on coarse momentum-transfer fingerprints with automated lattice nudging and physics-constrained Rietveld verification, enabling dominant-phase hypotheses to be generated from elemental constraints and secondary phases to be isolated recursively. On an experimental RRUFF PXRD benchmark, RADAR-PD outperforms DARA in recovering the reference phase. RADAR-PD further provides robust multiphase analysis on complex time-of-flight and constant-wavelength neutron datasets, addressing an important unmet need in automated neutron diffraction analysis. These results establish RADAR-PD as an auditable, instrument-agnostic framework for autonomous structural discovery.