Capturing local compositional fluctuations in NMR modelling of solid solutions.

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Understanding atomic-scale properties of solid solutions is key to structure-property relationships.
• Existing models fail to capture local compositional fluctuations affecting NMR spectra.

Purpose of the Study:

• To develop a computational approach for investigating local chemical environments in solid solutions.
• To address limitations of canonical ensemble models in capturing compositional fluctuations.

Main Methods:

• Combined solid-state nuclear magnetic resonance (NMR) spectroscopy with density functional theory (DFT) calculations.
• Employed a grand-canonical ensemble approach to represent diverse local chemical environments.
• Utilized ensemble truncation and machine learning (ML) to reduce computational cost.

Main Results:

• The grand-canonical ensemble approach provides a comprehensive NMR spectrum representation.
• Ensemble truncation and ML significantly reduced computational cost while preserving predictive power.
• Successfully modeled NMR spectra in a La₂(Zr₁₋ₓSnₓ)₂O₇ pyrochlore solid solution.

Conclusions:

• The combined grand-canonical, ML, and truncation approach offers an efficient framework for modeling NMR spectra in disordered crystalline materials.
• This method enhances the interpretation of NMR spectra by accounting for local chemical variations.