An Ab Initio Study and Machine Learning Framework to Capture the Motional Effects in Solid-State NMR of Lithium-Ion Conductors

Key Points

• This research aims to enhance the understanding of motional effects in solid-state NMR for lithium-ion conductors using a machine learning approach.
• Utilized solid-state NMR spectroscopy combined with density functional theory for atomic-scale analysis.
• Developed a machine learning framework to improve the capture of motional dynamics.
• Conducted simulations to predict and analyze lithium-ion conductivity in various solid-state materials.
• Achieved improved characterization of lithium-ion conductors with high sensitivity.
• Found significant correlation between predicted motional effects and experimental NMR data.
• Demonstrated a novel approach with enhanced predictive capabilities for ion conduction.