Enabling single-observation decomposition of multi-phase X-ray diffraction patterns via generative deep learning
Source abstract
Abstract
Powder X-ray diffraction (PXRD) is a vital technique for the structural characterization of crystalline compounds. However, this analysis is challenged by practically encountered multi-phase systems, whose mixed PXRD patterns necessitate prior phase decomposition. Traditional approaches require multiple mixture samples or prior knowledge of the constituent phases, limiting their applicability in complex or high-throughput scenarios. Here we show that multi-phase PXRD patterns from a single observation can be automatically decomposed into their constituent single-phase patterns using machine learning. The proposed solution—Phase Decomposition Diffusion Transformers (PhaseDifformer)—reinterprets the denoising process of diffusion models as a probabilistic regressor, enabling recursive extraction of unknown constituent phases. We validate the method on both synthetic mixtures and experimental measurements, achieving accurate phase decomposition in both cases. Given ongoing progress in single-phase PXRD-to-structure methods, our study fills a critical gap toward realizing fully automated, end-to-end analysis of complex PXRD patterns commonly encountered in materials research.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · X-ray Diffraction in Crystallography · Inorganic Chemistry and Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (57)
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