End-to-End Deep Learning Models for Predicting the Electrical Conductivity of Ionic Liquids
This study furnishes an efficient, robust, and COSMO-RS-independent computational toolkit for high-throughput virtual screening of conductive IL candidates and pinpointed temperature, molecular geometry, polarizability distribution, and nitrogen-bearing fragments as dominant conductivity modulators.