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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Spectroscopy

Background:

  • Understanding the local atomic coordination environment is crucial for explaining functional material properties and mechanisms.
  • Detecting and quantifying subtle structural changes under operando conditions remains a significant challenge due to local nature, small variations, and extreme experimental settings.

Purpose of the Study:

  • To develop and demonstrate an artificial neural network approach for extracting local structural information directly from X-ray absorption fine structure (XAFS) spectra.
  • To track in situ structural changes within materials during operational or dynamic processes.

Main Methods:

  • Utilized an artificial neural network (ANN) model trained on XAFS data.
  • Extracted the radial distribution function (RDF) from XAFS spectra to analyze local atomic arrangements.
  • Applied the method to bulk iron undergoing a temperature-induced phase transition.

Main Results:

  • Successfully extracted the radial distribution function (RDF) for both ferritic and austenitic phases of iron.
  • Quantified changes in iron coordination and material density across the temperature-induced transition.
  • Observed the structural transformation from body-centered cubic (BCC) to face-centered cubic (FCC) iron.

Conclusions:

  • The ANN-based XAFS analysis provides a powerful tool for in situ characterization of local atomic structures and phase transitions.
  • This method offers a viable approach for studying a wide range of materials under diverse experimental conditions.
  • The technique enables quantitative analysis of coordination environment and density changes, crucial for materials design and understanding.