What question did this study set out to answer?

Develop an efficient ab initio statistical mechanical framework to predict finite-temperature paramagnetic Fermi contact NMR shifts in complex, disordered materials.

August 16, 2026Journal of the American Chemical SocietyOpen Access

A Statistical MechanicalFramework for PredictingFermi Contact NMR Shifts

View Full Paper

Ask AI

Bookmark

Develop an efficient ab initio statistical mechanical framework to predict finite-temperature paramagnetic Fermi contact NMR shifts in complex, disordered materials.
Combined magnetic cluster expansion techniques with Monte Carlo sampling to compute finite-temperature paramagnetic shifts from first principles.
Applied the framework to model 7Li and 17O NMR shifts in the battery cathode material Li2MnO3.
Identified that nearest- and next-nearest-neighbor Mn-O-Li interaction pathways dominate 7Li Fermi contact shifts.
Uncovered significant long-range contributions to 17O Fermi contact shifts mediated by Mn-O-Mn-O pathways for the first time.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Computational modeling study demonstrates accurate prediction of finite-temperature Fermi contact shifts in Li2MnO3, uncovering key magnetic pathways in disordered materials.

Cite This Study

Bassey et al. (2026) studied this question.

synapsesocial.com/papers/6a817a60f2fb91fc834ae2cf https://doi.org/10.1021/jacs.6c07045

View Full Paper

Ask AI

Bookmark

Implication

Computational modeling study demonstrates accurate prediction of finite-temperature Fermi contact shifts in Li2MnO3, uncovering key magnetic pathways in disordered materials.

A Statistical MechanicalFramework for PredictingFermi Contact NMR Shifts

Key Points

Discussion

Implication

Cite This Study

A Statistical MechanicalFramework for PredictingFermi Contact NMR Shifts

Key Points

Discussion

Implication

Cite This Study

Keep reading

Keep reading