arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2501.07321physics.chem-phcond-mat.dis-nncond-mat.mtrl-scicond-mat.softphysics.comp-ph
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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

Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning

Qi You, Yan Sun, Feng Wang, Jun Cheng, Fujie Tang

AI总结:

本研究通过机器学习预测LiFSI/DME电解质中动态$^7$Li核磁共振化学位移,揭示两种竞争溶剂化结构在高浓度下的优势转变,解释化学位移反常逆转,为电解质设计提供分子级见解。

摘要:

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相互作用使得通过光谱方法对其“结构-性质”关系进行准确建模和解释更具挑战性。在本研究中,我们提出了一种基于机器学习的方法来预测LiFSI/DME电解质溶液中动态的$^7$Li核磁共振化学位移。此外,我们提供了全面的结构分析,以解释我们实验中观察到的化学位移行为,特别是高浓度下$^7$Li化学位移的突变。利用先进的建模技术,我们定量建立了分子结构与核磁共振谱之间的关系,为溶剂化结构归属提供了关键见解。我们的发现揭示了两种竞争的局部溶剂化结构的共存,随着电解质浓度接近浓缩极限,这两种结构的优势地位发生转变,导致我们实验中$^7$Li核磁共振化学位移的反常逆转。这项工作提供了对核磁共振光谱探测的复杂溶剂化结构的详细分子水平理解,为改进电解质设计铺平了道路。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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