Title: Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning Show Chinese title

Title: 通过机器学习解码动态溶剂化结构对电池电解质核磁共振化学位移的竞争效应

Abstract: Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties such as ionic conductivity, viscosity, and electrochemical stability. The highly complex structures and strong interactions in high-concentration electrolytes make accurate modeling and interpretation of their ``structure-property" relationships even more challenging with spectroscopic methods. In this study, we present a machine learning-based approach to predict dynamic $^7$Li NMR chemical shifts in LiFSI/DME electrolyte solutions. Additionally, we provide a comprehensive structural analysis to interpret the observed chemical shift behavior in our experiments, particularly the abrupt changes in $^7$Li chemical shifts at high concentrations. Using advanced modeling techniques, we quantitatively establish the relationship between molecular structure and NMR spectra, offering critical insights into solvation structure assignments. Our findings reveal the coexistence of two competing local solvation structures that shift in dominance as electrolyte concentration approaches the concentrated limit, leading to anomalous reverse of $^7$Li NMR chemical shift in our experiment. This work provides a detailed molecular-level understanding of the intricate solvation structures probed by NMR spectroscopy, leading the way for enhanced electrolyte design. Show Chinese abstract

Abstract: 理解电解质的溶剂化结构对于优化可充电电池的电化学性能至关重要，因为它直接影响离子电导率、粘度和电化学稳定性等性质。 高浓度电解质中高度复杂的结构和强烈的相互作用使得通过光谱方法准确建模和解释其“结构-性能”关系变得更加具有挑战性。 在本研究中，我们提出了一种基于机器学习的方法，用于预测LiFSI/DME电解质溶液中的动态$^7$锂核磁共振化学位移。 此外，我们提供了全面的结构分析，以解释实验中观察到的化学位移行为，特别是高浓度下$^7$锂化学位移的突然变化。 通过先进的建模技术，我们定量建立了分子结构与核磁共振光谱之间的关系，为溶剂化结构的确定提供了关键见解。 我们的研究结果揭示了两种竞争性的局部溶剂化结构共存的现象，随着电解质浓度接近高浓度极限，这两种结构的主导地位会发生变化，导致实验中$^7$锂核磁共振化学位移出现异常反转。 这项工作提供了对NMR光谱探测的复杂溶剂化结构的详细分子级理解，为改进电解质设计指明了方向。