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

Benjamin Zelin; Eleanor Davison; Saiful Islam; Jonathan Robert Yates; Andrey Poletayev; Gregory J. Rees; Bartholomew T Payne; Peter G. Bruce

doi:10.1039/d6ta02026g

January 1, 2026Journal of Materials Chemistry AOpen Access

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

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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.

Implication

Explores motional effects in lithium-ion conductors, indicating advanced analysis techniques.

Abstract

Solid-state NMR spectroscopy, when combined with first-principles density functional theory (DFT) calculations, offers a highly sensitive probe of atomic-scale structure and dynamics in solid-state ion conductors, enabling the characterisation of...

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Cite This Study

Zelin et al. (2026) studied this question.

synapsesocial.com/papers/6a211852d499ed480b170e45 https://doi.org/10.1039/d6ta02026g

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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