Lin Min, Xiong Jingfang, Su Mintao, Wang Feng, Liu Xiangsi, Hou Yifan, Fu Riqiang, Yang Yong, Cheng Jun
Collaborative Innovation Center of Chemistry for Energy Materials, State Key Laboratory for Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University Xiamen 361005 China
National High Magnetic Field Laboratory 1800 E. Paul Dirac Drive Tallahassee FL 32310 USA.
Chem Sci. 2022 Jun 13;13(26):7863-7872. doi: 10.1039/d2sc01306a. eCollection 2022 Jul 6.
Solid-state nuclear magnetic resonance (ssNMR) provides local environments and dynamic fingerprints of alkali ions in paramagnetic battery materials. 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 battery materials is still challenging because the local structures and dynamic information of alkali ions are highly correlated and difficult to acquire. Herein, 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 the accuracy of density functional theory (DFT). Using structurally well-defined P2-type Na(MgMn)O as an example, we validate the ML protocol and show the significance of dynamic effects on chemical shifts. Moreover, with the protocol, it is demonstrated that the two experimental Na shifts (1406 and 1493 ppm) of P2-type Na(NiMn)O originate from two stacking sequences of transition metal (TM) layers for the first time, which correspond to space groups 6/ and 622, 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.
固态核磁共振（ssNMR）可提供顺磁性电池材料中碱金属离子的局部环境和动态特征。将局部离子环境与核磁共振信号联系起来需要使用已开发了十多年的昂贵的第一性原理计算工具。然而，对高倍率电池材料的动态核磁共振谱进行归属仍然具有挑战性，因为碱金属离子的局部结构和动态信息高度相关且难以获取。在此，我们开发了一种新颖的机器学习（ML）方法，该方法不仅可以快速采样原子构型，还能高效预测化学位移，这使我们能够以密度泛函理论（DFT）的精度计算动态核磁共振位移。以结构明确的P2型Na（MgMn）O为例，我们验证了该ML方法，并展示了动态效应对化学位移的重要性。此外，通过该方法首次证明了P2型Na（NiMn）O的两个实验钠位移（1406和1493 ppm）分别源自过渡金属（TM）层的两种堆叠序列，它们分别对应于空间群6/和622。这种ML方法有助于将动态ssNMR谱与局部结构以及碱金属离子的快速传输相关联，有望适用于广泛的快速动态系统。
