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Kantor, Innokenty

Publications and source records attributed to Kantor, Innokenty.

Pressure-induced charge-transfer and structural transition in hexagonal multiferroic HoMnO 3

The structural properties of the hexagonal multiferroic ℎ-HoMnO 3 under high pressure have been explored using synchrotron x-ray diffraction and x-ray absorption spectroscopy in diamond anvil cells. The structure was found to undergo a pressure-induced phase transition at ~24 GPa to a rhombohedrally distorted superstructure, which is isostructural to the oxygen-loaded h-RMnO 3+δ (R = Y,Dy,Ho,Er; δ ≈ 0.28) phases found in the same systems. The driving force behind the phase transition is the highly compressible ab plane which facilitates a gradual charge disproportionation of Mn(III) with pressure. We speculate this stabilizes the spin-liquid phase due to ferromagnetic coupling between neighboring Mn(II)/Mn(IV) and Mn(III). In addition, we demonstrate that the structural behavior is highly susceptible to nonhydrostatic conditions and the choice of pressure medium should be carefully made.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

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↗