skip to main content

DOE PAGESDOE PAGES

This content will become publicly available on May 25, 2019

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

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
Authors:
 [1] ; ORCiD logo [2] ;  [2] ;  [2] ;  [2] ;  [3]
  1. Stony Brook Univ., Stony Brook, NY (United States)
  2. Univ. of Latvia, Riga (Latvia)
  3. Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Report Number(s):
BNL-203623-2018-JAAM
Journal ID: ISSN 0031--9007
Grant/Contract Number:
SC0012704; FG02-03ER15476; 18-047; 20160412
Type:
Accepted Manuscript
Journal Name:
Phys. Rev. Letters
Additional Journal Information:
Journal Volume: 120; Journal Issue: 22; Journal ID: ISSN 0031--9007
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY
OSTI Identifier:
1436268
Alternate Identifier(s):
OSTI ID: 1439742