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Skjærvø, Susanne Linn

Publications and source records attributed to Skjærvø, Susanne Linn.

High-Entropy Oxides in the Mullite-Type Structure

High-entropy materials (HEMs) represent a new class of solid solutions containing at least five different elements. Their compositional diversity makes them promising as platforms for the development of functional materials. We synthesized new HEMs in a mullite-type structure and present five compounds, i.e., Bi 2 (Al 0.25 Ga 0.25 Fe 0.25 Mn 0.25 ) 4 O 9 and A 2 Mn 4 O 10 with variations of A = Nd, Sm, Y, Er, Eu, Ce, and Bi, demonstrating the vast accessible composition space. By combining scattering, microscopy, and spectroscopy techniques, we show that our materials are mixed solid solutions. Remarkably, when following their crystallization in situ using X-ray diffraction and X-ray absorption spectroscopy, we find that the HEMs form through a metastable amorphous phase without the formation of any crystalline intermediates. We expect that our synthesis is excellently suited to synthesizing diverse HEMs and therefore will have a significant impact on their future exploration.

36 MATERIALS SCIENCE↗

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.

36 MATERIALS SCIENCE↗