Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy.

Area of Science:

• Materials Science
• Spectroscopy
• Machine Learning

Background:

• Determining atomistic material structures, especially with defects, is crucial but challenging.
• Existing experimental and computational methods have limitations in nanoscale resolution.
• Single-source spectroscopic data (e.g., XAS, EELS) can be ambiguous due to similar spectral features from different structures.

Purpose of the Study:

• To develop a framework for accurate material structure characterization using multimodal spectroscopic data and machine learning.
• To overcome the limitations of single-data-stream approaches in differentiating competing structural hypotheses.
• To determine local structures and properties of materials by integrating data from multiple elements and spectroscopic edges.

Main Methods:

• Integration of multimodal ab initio simulations and experimental data acquisition.
• Application of machine learning techniques to analyze electron energy-loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) data.
• Utilizing various lithium nickel manganese cobalt (NMC) oxide compounds, including those with defects, as a model system.

Main Results:

• Successfully inferred local element content (e.g., lithium, transition metals) with quantitative agreement.
• Demonstrated the capability of the multimodal machine learning model to detect local defects like oxygen vacancies and antisites.
• Achieved physical interpretability, connecting spectroscopic data to local atomic and electronic structures.

Conclusions:

• The multimodal spectroscopic and machine learning framework provides a powerful tool for accurate material structure characterization.
• This approach overcomes the ambiguity of single-source spectroscopic data, enabling reliable defect identification.
• The framework offers physical insights, bridging experimental spectroscopy with fundamental material properties.