Algorithm for distance list extraction from pair distribution functions.

Area of Science:

• Materials Science
• Computational Chemistry
• Data Analysis

Background:

• Atomic pair distribution functions (PDFs) are crucial for characterizing materials at the atomic scale.
• Extracting precise distance information from PDFs can be computationally challenging due to complex data and optimization problems.

Purpose of the Study:

• To develop a highly automated algorithm for extracting atomic distance lists from PDFs.
• To address the computational difficulties associated with non-convex optimization problems in PDF analysis.
• To improve the accuracy and efficiency of atomic structure determination from scattering data.

Main Methods:

• Curve fitting based on the Debye scattering equation model.
• Development of techniques to overcome computational challenges in non-convex optimization.
• Implementation of a novel approach for generating a reasonable initial guess using theoretical model properties.

Main Results:

• The algorithm successfully extracts distance lists from atomic pair distribution functions in a highly automated manner.
• Tests on various nanostructured samples demonstrate the effectiveness of the initial guess strategy.
• The algorithm shows accuracy and good overall performance in atomic distance extraction.

Conclusions:

• The developed algorithm provides an effective and automated method for atomic distance extraction from PDFs.
• The novel initial guess approach significantly contributes to the algorithm's success.
• The methodology shows potential for extension to other spectral analyses approximated by Gaussian functions.