A dynamic routing-guided interpretable framework for salt-solvent chemistry

Salt-solvent chemistry underpins electrochemical systems, governing key properties such as ionic conductivity, viscosity and chemical stability. Yet, its rational design is hindered by the vast chemical space spanning countless combinations and nonlinear structure-behavior couplings, further amplified by sparse and imbalanced experimental data that impede generalization. Here we develop SCAN, a dynamic routing-guided framework for modeling and interpreting salt-solvent chemistry, which effectively handles long-tailed data and captures the full spectrum of salt-solvent formulations. We apply SCAN to non-aqueous electrolytes and achieve a benchmark error of 0.372 mS cm^-1 on conductivity, reducing predictive error by 65.3% over baselines. Then, we shape the conductivity atlas across 11,515,140 salt-solvent systems. Importantly, large-scale validations confirm a success rate of 81.08% for top-predicted candidates, including LiFSI-, LiTFSI- and LiBOB-based systems with conductivity >20 mS cm^-1. Beyond prediction, SCAN provides chemical insight into how molecular flexibility and ion-solvent interactions influence conductivity by incorporating the gradient-decoupling approach, symbolic regression and quantum chemistry calculation.