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

TL;DR

This paper uses machine learning to predict and interpret $^7$Li NMR chemical shifts in battery electrolytes, revealing competing solvation structures that influence electrochemical performance and aiding in electrolyte design.

Contribution

It introduces a machine learning approach to model dynamic solvation structures and interpret NMR shifts, providing new insights into electrolyte behavior at high concentrations.

Findings

Identified two competing local solvation structures affecting NMR shifts.

Demonstrated the shift in dominance of solvation structures with concentration.

Revealed anomalous reverse of $^7$Li NMR chemical shift at high concentration.

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.