Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

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Angewandte Chemie Int Ed, Wiley-VCH

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Angewandte Chemie International Edition, EarlyView.

An artificial intelligence‐driven approach using graph neural network was employed to analyze 75 024 organic molecules, among which 48 promising candidate molecules were identified by this high‐throughput screening method, and cyanoacetamide and hydantoin were experimentally validated as novel electrolyte additives to improve stability and reversibility of the zinc anode in aqueous zinc‐ion batteries. Abstract Currently, challenges such as zinc dendrites, hydrogen evolution reactions, and byproduct formation on the zinc anode damage the performance and cycling stability of aqueous zinc‐ion batteries (AZIBs). Electrolyte additives, especially organic molecule additives, provide an effective and cost‐efficient strategy to address these issues. To efficiently screen a large number of organic molecules for developing new electrolyte additives, we employ an artificial intelligence‐driven approach, using graph neural network to analyze 75 024 organic molecules based on three key properties, including adsorption energies on Zn(002) surface, redox potentials, and water solubility. We identified 48 promising candidate molecules by this high‐throughput screening method, among which cyanoacetamide (CA) and hydantoin (HN) were experimentally validated as novel electrolyte additives for AZIBs that have not been reported previously. The experimental and calculation results demonstrate that CA and HN preferentially adsorb onto the surface of the zinc anode, resulting in the enhanced interfacial stability of zinc anodes. This behavior effectively mitigates zinc dendrite formation, contributing to the improved stability and reversibility of the zinc electrode. It is believed that our work combines AI‐assisted high‐throughput research, experimental validation, and theoretical calculations, providing a scalable framework for selecting and developing new electrolyte additive molecules.

An artificial intelligence-driven approach using graph neural network was employed to analyze 75 024 organic molecules, among which 48 promising candidate molecules were identified by this high-throughput screening method, and cyanoacetamide and hydantoin were experimentally validated as novel electrolyte additives to improve stability and reversibility of the zinc anode in aqueous zinc-ion batteries.

Abstract

Currently, challenges such as zinc dendrites, hydrogen evolution reactions, and byproduct formation on the zinc anode damage the performance and cycling stability of aqueous zinc-ion batteries (AZIBs). Electrolyte additives, especially organic molecule additives, provide an effective and cost-efficient strategy to address these issues. To efficiently screen a large number of organic molecules for developing new electrolyte additives, we employ an artificial intelligence-driven approach, using graph neural network to analyze 75 024 organic molecules based on three key properties, including adsorption energies on Zn(002) surface, redox potentials, and water solubility. We identified 48 promising candidate molecules by this high-throughput screening method, among which cyanoacetamide (CA) and hydantoin (HN) were experimentally validated as novel electrolyte additives for AZIBs that have not been reported previously. The experimental and calculation results demonstrate that CA and HN preferentially adsorb onto the surface of the zinc anode, resulting in the enhanced interfacial stability of zinc anodes. This behavior effectively mitigates zinc dendrite formation, contributing to the improved stability and reversibility of the zinc electrode. It is believed that our work combines AI-assisted high-throughput research, experimental validation, and theoretical calculations, providing a scalable framework for selecting and developing new electrolyte additive molecules.

Guangsheng Xu, Yue Li, Junfeng Li, Jinliang Li, Xinjuan Liu, Chenglong Wang, Wenjie Mai, Guang Yang, Likun Pan

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