Carbonyl‐Modulated Lowest Unoccupied Molecular Orbital Energy Directs Machine Learning‐Assisted Screening of Electrolyte Additives Toward Ultra‐Stable Zinc Metal Anodes

Abstract

ABSTRACT The commercialization of aqueous zinc‐ion batteries has long been hindered by side reactions stemming from zinc anode interfacial instability. Organic molecular additives offer an effective solution. Here, using lowest unoccupied molecular orbital (LUMO) energy and solubility as dual screening criteria, a novel high‐precision Organic Molecular Attention Prediction Graph Neural Network is developed to enable high‐throughput screening of organic additives. Through Shapley Additive exPlanations and density of states calculations, carbonyl electron localization is established as the dominant descriptor governing interfacial dynamics. α‐ketoglutaric acid (Ket) was selected as the optimal additive based on this principle. Strong coordination between its electronegative carbonyl groups enables the formation of a gradient‐structured solid‐electrolyte interphase on the Zn surface, resulting in uniform Zn 2+ flux distribution and significantly enhancing interfacial reversibility. Experimental demonstrates Zn||Cu cells achieve a high average Coulombic efficiency of 99.93% over 3500 cycles, while Zn||Zn cells exhibit unprecedented longevity of 4550 h (187 days) at 5.0 mA·cm −2 with calendar life exceeding 7000 h, and maintain stability even at ultra‐high current densities of 30 mA·cm −2 . Full cells paired with high‐loading (∼10 mg cm −2 ) ammonium vanadate cathodes retain over 80% capacity after 600 cycles. This study establishes a closed‐loop framework of screening, providing a new pathway for metal battery systems.