MULTI-OBJECTIVE OPTIMIZATION AND ROBUST SCREENING MODELING OF AQUEOUS ELECTROLYTE FORMULATIONS

Aqueous electrolyte formulation optimization involves coupled conductivity, acid-base compatibility, electrochemical stability, and preparation robustness under limited experimental budgets. Based on 251 experimental records, this study constructs an integrated modeling framework for performance evaluation, predictive modeling, mechanism interpretation, reliability assessment, and candidate formulation screening. The original stock-solution volumes, molalities, densities, component existence indicators, ionic load, mass load, Li/Na ratio, anion-family proportions, water volume fraction, and component entropy are organized into a unified feature space. Conductivity, pH, and the electrochemical stability window measured at the 1 mA/cm2 threshold are then combined with robustness indicators to support multi-objective formulation ranking. Ridge regression, random forest, and Gaussian process regression are compared under random and structural validation. The Gaussian process model performs best for conductivity prediction, while random forest provides the strongest pH and stability-window predictions. Feature importance and response analysis show that total ion load dominates conductivity, Li/Na-related descriptors influence pH, and NaBr-related descriptors strongly affect the electrochemical window. Candidate formulations are further screened through Bayesian optimization and local perturbation analysis, identifying NaClO4-NaNO3 systems as high-potential and robust candidates for subsequent experiments.