Unveiling Solvation Structure Dynamics in Lithium-Ion Battery Electrolytes Through Enhanced Sampling and Machine Learning

Understanding solvation structure dynamics in liquid electrolytes is critical to the performance of lithium-ion batteries, particularly as solvation environments directly influence electrolyte stability, ion transport, and interactions with electrodes. At higher solute concentrations, reduced solvent availability leads to complex and diverse local solvation structures, a phenomenon often referred to as "entropy starvation." These intricate dynamics are challenging to capture with classical Molecular Dynamics (MD) simulations, which may suffer from limited configurational sampling and bias from initial conditions. In this work, we employ advanced sampling techniques, including metadynamics and umbrella sampling, to thoroughly explore the configurational landscape of lithium-ion solvation in LiCl salt solutions. Coupled with machine learning algorithms, such as unsupervised clustering, we extract distinct solvation geometries from MD trajectories, overcoming the limitations of traditional ensemble-averaged metrics like radial distribution functions (RDFs) and coordination numbers. This novel integration of techniques enables the identification of solvation structures and the calculation of unbiased probabilities, revealing a detailed free energy landscape that provides deeper insight into solvation behavior at varying concentrations. This methodology extends beyond simple salt solutions and can be applied to a wide range of battery electrolyte systems, offering a robust framework for studying phase behavior, transport mechanisms, and the solubility limits of electrolyte materials. Our approach represents a significant step forward in understanding the role of solvation structure in the performance and durability of lithium-ion batteries, with broader implications for advanced energy storage technologies.