Details

Autor(en) / Beteiligte

• Timoshenko, Janis
• Frenkel, Anatoly I.
• Cintins, Arturs
• Kuzmin, Alexei
• Purans, Juris

Titel

Neural network approach for characterizing structural transformations by X-ray absorption fine structure

Ist Teil von

• Physical review letters, 2018-05, Vol.120 (22)

Ort / Verlag

United States: American Physical Society (APS)

Erscheinungsjahr

2018

Quelle

American Physical Society

Beschreibungen/Notizen

• The knowledge of coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use artificial neural network approach to extract the information on the local structure and its in-situ changes directly from the X-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic and austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from body-centered to face-centered cubic arrangement of iron atoms. Furthermore, this method is attractive for a broad range of materials and experimental conditions