Data-Driven Additive Discovery with HOMO-Descriptor Enables Durable Aqueous Zinc Batteries via Interfacial Kinetics Engineering.

Dendritic growth and parasitic reactions severely hinder aqueous Zn-ion batteries due to interfacial instability and uncontrolled charge transfer. Here, a machine learning-accelerated strategy for rational additive screening, establishing a predictive framework that links the highest occupied molecular orbital (HOMO) energy level to the adsorption and reduction behavior of Zn, is reported. An interpretable machine learning model (Adaptive Boosting), trained on a curated molecular dataset, achieves high accuracy (Mean Squared Error = 0.2977, Pearson Correlation Coefficient = 0.8032) in HOMO prediction. Guided by this model, 4-dimethylaminopyridine is identified as a high-performance additive, which can suppress Zn dendrite formation by slowing interfacial charge transfer and mitigating local ion starvation through kinetic matching between mass transport and deposition. Moreover, 4-dimethylaminopyridine effectively excludes interfacial HO molecules, significantly inhibiting parasitic reactions. Consequently, Zn anode delivers high reversibility of plating/stripping with an average coulombic efficiency of 99.85% over 1600 cycles. The 0.3-Ah NaVO·1.5HO|Zn pouch cell delivers stable cyclability for 70 days, with a capacity retention of 73% after 250 cycles. This work pioneers the integration of machine learning with interfacial electrochemistry, offering a generalizable approach for additive discovery and electrolyte design, and sets a new paradigm for achieving dendrite-free metallic anodes in aqueous systems.