The utilization of electrolyte additives has been regarded as an efficient strategy to construct dendrite-free aqueous zinc-ion batteries (AZIBs). However, the blurry screening criteria and time-consuming experimental tests inevitably restrict the application prospect of the electrolyte additive strategy. With the rise of artificial intelligence technology, machine learning (ML) provides an avenue to promote upgrading of energy storage devices. Herein, we proposed an intriguing ML-assisted method to accelerate the development efficiency of electrolyte additives on dendrite-free AZIBs. Concretely, we selected the Gutmann donor number (DN value) as a screen parameter, which can reflect the interaction between solvent molecules and ions, and proposed an integrated ML model that can predict the DN values of organic molecules via molecular fingerprints, thereby achieving the screening of electrolyte additives. Then, combined with experimental tests and theoretical calculations, the influence law of three additive molecules with different DN values on the thermodynamic stability of the Zn anode and its corresponding optimization mechanisms were revealed; the DN values of the additives are in positive correlation with the electrochemical performance of the Zn anode. Especially, an isopropyl alcohol (IPA) additive with a high DN value (36) integrated with various Zn-based cells presented a superior electrochemical performance, including a high calendar life (1500 h), a stable Coulombic efficiency (99% within 450 cycles), and a favorable cycling retention. This work pioneers ML techniques for predicting DN values for electrolyte additives, offering a compelling investigation method for the investigation of AZIBs.

Machine Learning-Assisted High-Donor-Number Electrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion Batteries