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Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Authors :
Jeong, Wonseok
Sun, Wenyu
Calegari Andrade, Marcos F.
Wan, Liwen F.
Willey, Trevor M.
Nielsen, Michael H.
Pham, Tuan Anh
Source :
Chemistry of Materials; May 2024, Vol. 36 Issue: 9 p4144-4156, 13p
Publication Year :
2024

Abstract

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. We discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

Details

Language :
English
ISSN :
08974756
Volume :
36
Issue :
9
Database :
Supplemental Index
Journal :
Chemistry of Materials
Publication Type :
Periodical
Accession number :
ejs66387326
Full Text :
https://doi.org/10.1021/acs.chemmater.3c02957