Authors: X. C. Ruan | Tod A. Pascal | Brett A. Helms | Carson Zellmann | David Prendergast | Fabrice Roncoroni
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Unveiling Solvation Structure Landscapes for Sodium Single-Ion Conductor Using Enhanced Sampling, Thermodynamic Corrections, and Unsupervised Learning Understanding how ions are solvated at the molecular level is essential for advancing electrolyte and catalyst design in electrochemical systems, as solvation critically influences ion transport, conductivity, solubility, and phase stability. However, simulating concentrated electrolytes using molecular dynamics (MD) presents persistent challenges due to finite-size effects and inefficient sampling, which can artificially stabilize certain solvation structures and hinder exploration of the full configurational space. To address these issues, we present a robust and generalizable approach that integrates enhanced sampling via Metadynamics, chemical potential corrections, and unsupervised machine learning for direct, high-resolution solvation structure analysis (Figure 1). Crucially, we move beyond conventional collective variables (CVs) by redefining the sampling space using solvation geometries as microstates. Instead of relying on predefined descriptors like coordination numbers or distances, we harness Metadynamics trajectories and project them onto a solvation-structure-defined space, enabling free energy reconstruction directly in terms of physically meaningful configurations. This allows us to extract rich thermodynamic and structural information without requiring quantum mechanical calculations for the free energy of each solvation structure—offering significant computational efficiency. Through structure extraction, dimensionality reduction, clustering, and cluster alignment, we resolve the diverse local environments that are often obscured by ensemble averaging methods. We first apply this method to aqueous LiCl solutions to investigate solubility behavior. In high-concentration systems, applying bias to multiple Li⁺ ions makes it difficult to separate individual contributions and can introduce artifacts. Instead, we bias a single Li⁺ ion to enable efficient sampling while avoiding these issues. Free energy profiles of solvation structures are reconstructed using statistical mechanics via biased probability reweighting. To correct for unphysically large cluster formation that may arise at high concentrations, we incorporate a chemical potential correction based on the number of LiCl units. This allows us to construct a physically meaningful solvation free energy model that accounts for activity coefficients, composition, and distinct solvation environments. Our results reveal a transition from solvent-separated and contact ion pairs at low concentrations to highly coordinated (LiCl)ₓ aggregates at elevated concentrations—mirroring experimentally observed solubility limits between 283–313 K (298K results in Figure 2). We further apply this workflow to a more complex system—a tetrameric Na single-ion conductor (Figure 3)—starting with custom MD force field parameters based on quantum mechanical and empirical methods to accurately sample its rich solvation behavior. This allows us to explore how solvation structures facilitate ion transport and how geometries with identical chemical compositions can possibly exhibit functionally distinct transport properties. Figure 1 Solvation ; Metadynamics ; Curse of dimensionality ; Molecular dynamics ; Ion ; Electrolyte ; Implicit solvation ; Energy landscape ; Multiscale modeling Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Battery Materials and Technologies