Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy

Janis Timoshenko, Andris Anspoks, Arturs Cintins, Alexei Kuzmin, Juris Purans, and Anatoly I. Frenkel

Phys. Rev. Lett. 120, 225502 – Published 31 May 2018

Abstract

The knowledge of the 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 an 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 a body-centered to a face-centered cubic arrangement of iron atoms. This method is attractive for a broad range of materials and experimental conditions.

Received 23 February 2018

DOI:https://doi.org/10.1103/PhysRevLett.120.225502

Physics Subject Headings (PhySH)

Phase transitions Structural properties

Body-centered cubic Crystalline systems Face-centered cubic

Machine learning Molecular dynamics X-ray absorption fine structure spectroscopy

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Janis Timoshenko^1,*, Andris Anspoks², Arturs Cintins², Alexei Kuzmin², Juris Purans², and Anatoly I. Frenkel^1,3,†

¹Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, USA
²Institute of Solid State Physics, University of Latvia, Kengaraga Street 8, Riga, LV-1063, Latvia
³Division of Chemistry, Brookhaven National Laboratory, Upton, New York 11973, USA