ArticleAdvanced materials (Deerfield Beach, Fla.)2026
Carbonyl-Modulated Lowest Unoccupied Molecular Orbital Energy Directs Machine Learning-Assisted Screening of Electrolyte Additives Toward Ultra-Stable Zinc Metal Anodes.
Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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