Advances in modelling X-ray absorption spectroscopy data using reverse Monte Carlo

Area of Science:

• Materials Science
• Condensed Matter Physics
• Analytical Chemistry

Background:

• Modern extended X-ray absorption fine structure (EXAFS) analysis relies on complex multiple-scattering calculations.
• Accurate atomic configurations and disorder quantification are crucial for interpreting EXAFS data.
• The Reverse Monte Carlo (RMC) method has emerged as a powerful tool for generating structural models from experimental data.

Purpose of the Study:

• To extend and apply the RMC-GnXAS method to multiple-edge EXAFS studies.
• To investigate the method's performance for diverse materials including gases, crystalline solids, and liquids.
• To assess the impact of noise and long-range constraints on structural refinement accuracy.

Main Methods:

• Application of the Reverse Monte Carlo (RMC) method within the RMC-GnXAS framework.
• Analysis of multiple-edge EXAFS data for various atomic structures.
• Incorporation of long-range structural constraints from complementary techniques like diffraction.

Main Results:

• Successful extension of RMC-GnXAS to multiple-edge EXAFS analysis.
• Generation of accurate 3D atomic models for molecular gases (Br2, GeI4), crystalline solids (Ge, AgBr), and liquid (AgBr).
• Demonstration of improved structural refinement by combining multiple datasets and incorporating long-range information.

Conclusions:

• The RMC method is a valuable technique for EXAFS data analysis, providing consistent and useful structural models.
• Combining multiple EXAFS edges and incorporating long-range data significantly enhances structural refinement accuracy.
• Careful consideration of noise levels is essential for reliable RMC analysis.