End-to-End Deep Learning Models for Predicting the Electrical Conductivity of Ionic Liquids

Abstract

Ionic liquids (ILs) hold great promise for electrochemical technologies, yet their virtually infinite compositional space renders exhaustive experimental conductivity measurements infeasible. Existing QSPR models frequently suffer from limited generalizability when evaluated under strict IL-disjoint partitioning, and those that perform reasonably often depend on costly COSMO-RS computations. Here we present end-to-end multilayer perceptron (MLP) models that directly forecast IL electrical conductivity from a minimal set of readily accessible molecular features—temperature, Dragon structural descriptors, and quantum chemical parameters—entirely circumventing continuum solvation calculations. Two curated data sets were examined (Data Set I: 2168 records/242 ILs; Data Set II: 5297 records/629 ILs), each partitioned via rigorous IL-based splitting such that test sets comprised 48 and 157 completely unseen ILs, respectively. Our MLP architectures attained a test coefficient of determination (R2) of 0.871 with a mean absolute error (MAE) of 0.392 on Data Set I and R2 of 0.778 (MAE = 0.600) on Data Set II, on par with COSMO-RS-boosted alternatives while eliminating their computational overhead and convergence issues. A random forest control experiment further demonstrated that point-wise splitting inflates R2 to 0.979, whereas the identical model collapses to 0.627 under IL-disjoint validation, highlighting the critical role of proper data partitioning. SHAP-based interpretability pinpointed temperature, molecular geometry, polarizability distribution, and nitrogen-bearing fragments as dominant conductivity modulators. This study furnishes an efficient, robust, and COSMO-RS-independent computational toolkit for high-throughput virtual screening of conductive IL candidates.