A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials
Min Lin, Jing-Fang Xiong, Mintao Su, Feng Wang, Xiangsi Liu, Yi-Fan Hou, Riqiang Fu, Yong Yang, Jun Cheng
Abstract
22, respectively. This ML protocol could help to correlate dynamic ssNMR spectra with the local structures and fast transport of alkali ions and is expected to be applicable to a wide range of fast dynamic systems.
Topics & Concepts
Chemical shiftIonAlkali metalParamagnetismChemistryMolecular dynamicsSolid-state nuclear magnetic resonanceBattery (electricity)Ionic bondingNMR spectra databaseChemical physicsDensity functional theorySpectral lineMaterials scienceNuclear magnetic resonanceComputational chemistryPhysical chemistryPhysicsCondensed matter physicsOrganic chemistryAstronomyQuantum mechanicsPower (physics)Advanced NMR Techniques and ApplicationsSolid-state spectroscopy and crystallographyAtomic and Subatomic Physics Research