Theoretical And Experimental Studies Of Solid-State NMR Spectroscopy Of High-Rate Rechargeable Battery Cathode Materials

Solid-state nuclear magnetic resonance(ssNMR)technique has received extensive attention in characterizing battery cathode materials,due to it is highly sensitive for probing the local environment and dynamic information of alkali-ion.Linking the local ionic environments and NMR signals requires expensive first-principles computational tools that have been developed for over a decade.Nevertheless,the assignment of the dynamic NMR spectra of high-rate cathode materials is still challenging because the local structures and dynamic information of alkali-ions are highly correlated and hardly acquired.Herein,we develop the methods for calculating the dynamic NMR shift at density functional theory(DFT)accuracy.Combining with the computational methods and ssNMR,X-ray diffraction measurements,we reveal the fine structures of P2-type sodium-ion battery(SIBs)cathode materials,such as the superstructure of transition metal ions,stacking sequence of transition metal oxide layers,and the related Na+diffusion dynamics.Combining DFT calculation of paramagnetic shift and deep potential molecular dynamics(DPMD)simulation to achieve the converged Na+ distribution at hundreds of nanoseconds,we obtain the dynamic averaged NMR chemical shift which is in excellent agreement with ssNMR measurements.Accordingly,two 23Na shifts induced by different stacking sequences of transition metal layers are revealed in the fast chemically exchanged NMR spectra of P2-type Na2/3(Mg1/3Mn2/3)O2 for the first time.We develop a novel machine learning(ML)protocol that could not only quickly sample atomic configurations but also predict chemical shifts efficiently,which enables us to calculate dynamic NMR shifts with DFT accuracy.Using the structurally welldefined P2-type Na2/3(Mg1/3Mn2/3)O2 as an example,we validate the ML protocol and show the significance of dynamic effects on chemical shift.With the new protocol,it is demonstrated that two experimental 23Na shifts(1406 and 1493 ppm)of P2-type Na2/3(Ni1/3Mn2/3)O2 originate from two stacking sequences of transition metal(TM)layers for the first time,which correspond to space group P63/mcm and P6322,respectively.The ML protocol presented here can be easily extended to other fast dynamic systems,such as solutions,diamagnetic solid-state electrolytes(SSEs),etc.,and to other nuclei,which will stimulate future work.