Cited by Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

A Graph Neural Network-Based Approach to XANES Data Analysis DOI

Fei Zhan, Haodong Yao, Zhi Geng

et al.

The Journal of Physical Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between structural attributes materials and their functional performance. X-ray absorption near edge structure (XANES) an indispensable tool to characterize atomic-scale local 3D system. Here, we present approach simulate XANES based on a customized graph neural network (3DGNN) model, XAS3Dabs, which takes directly system as input, inherent relation fine spectrum geometry considered during model construction. It turns out be faster than traditional fitting method when simulation optimization algorithm are combined fit given geometric features included in weighted message passing block XAS3Dabs importance investigated. demonstrates superior accuracy prediction compared most machine learning models. By extracting graphs constituted by edges related absorbing atom, our reduces redundant information, thereby not only enhancing model's performance but also improving its robustness across different hyperparameters. can generalized spectra systems with absorber having designed so meet expectations online data processing. expected key part analysis framework XAS-related beamlines high-energy photon source (HEPS) now under

Language: Английский

Citations

Elucidating the Origins of High Capacity in Iron-Based Conversion Materials: Benefit of Complementary Advanced Characterization toward Mechanistic Understanding DOI

Ryan C. Hill, Kenneth J. Takeuchi

Accounts of Chemical Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

ConspectusLithium-ion batteries are recognized as an important electrochemical energy storage technology due to their superior volumetric and gravimetric densities. Graphite is widely used the negative electrode, its adoption enabled much of modern portable electronics landscape. However, developing markets, such electric vehicles grid-scale storage, have increased demands, including higher content a diverse materials supply chain. Alternatives that provide opportunity increase capacity address chain concerns interest.Understanding fundamental mechanisms govern battery function crucial driving further improvements in field. Advanced characterization techniques, those by synchrotron light sources high-resolution electron microscopes, can uncover these become necessity for elucidating structural evolution upon conversion at nano- mesoscales. Performing experiments with relevant electrochemistry using situ operando imparts ability identify critical reaction pathways capture intermediate (dis)charge products not discernible traditional experiments.This Account describes series recent studies focused on advanced spinel-type iron oxide-based anode materials. These begin magnetite (Fe3O4), low cost oxide which, when synthesized appropriate coprecipitation based crystallite size control, provides realize eight electrons per formula unit via reduction. We then transition bi- trimetallic ferrites (such ZnFe2O4 CoMnFeO4) conclude high-entropy spinel ferrite oxides (HEOs) contain least 5 metals. For each material type, variety techniques utilized describe rationalize behavior. X-ray absorption spectroscopy (XAS) featured prominently, it allows element specific analysis electronic structure local atomic environments, nanocrystalline conversion. Combining XAS-based diffraction microscopy, oxide-type electrodes from rock-salt metal nanoparticles full lithiation be deciphered. analogues, delithiation results return highly disordered network FeO-like domains. Notably, while appear limited reoxidation Fe 2+ state, through introduction entropy-induced stability, oxidation states (up 2.6+) accessed oxidation. may hold promise alternatives graphite where combination high compositional flexibility avenue toward low-cost, sustainable storage.

Language: Английский

Citations

Interpretable multimodal machine learning analysis of X-ray absorption near-edge spectra and pair distribution functions DOI

Tanaporn Na Narong,

Zoe N. Zachko,

Steven B. Torrisi

et al.

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 11, 2025

Language: Английский

Citations

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS DOI

Micah P. Prange, Niranjan Govind, Panos Stinis

et al.

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

The fact that the photoabsorption spectrum of a material contains information about atomic structure, commonly understood in terms multiple scattering theory, is basis popular extended X-ray absorption spectroscopy (EXAFS) technique. How much same structural present other complementary spectroscopic signals not obvious. Here we use machine learning approach to demonstrate within theoretical models accurately predict EXAFS signal, near-edge region does indeed contain EXAFS-accessible information. We do this by exhibiting deep operator neural networks (DeepONets) have learned relationship between and near edge portions former from latter. find can 6 14 Å

Language: Английский

Citations