AI‐Driven Electrolyte Additive Selection to Boost Aqueous Zn‐Ion Batteries Stability

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Abstract
In tackling the stability challenge of aqueous Zn‐ion batteries (AZIBs) for large‐scale energy storage, the adoption of electrolyte additive emerges as a practical solution. Unlike current trial‐and‐error methods for selecting electrolyte additives, a data‐driven strategy is proposed using theoretically computed surface free energy as a stability descriptor, benchmarked against experimental results. Numerous additives are calculated from existing literature, forming a database for machine learning (ML) training. Importantly, this ML model relies solely on experimental values, effectively addressing the challenge of large solvent molecule models that are difficult to handle with quantum chemistry computation. The interpretable linear regression algorithm identifies the number of heavy atoms in the additive molecule and the liquid surface tension as key factors. Artificial intelligence (AI) clustering categorizes additive molecules, identifying regions with the most significant impact on enhancing battery stability. Experimental verification successfully confirms the exceptional performance of 1,2,3‐butanetriol and acetone in the optimal region. This integrated methodology, combining theoretical models, data‐driven ML, and experimental validation, provides insights into the rational design of battery electrolyte additives.
Funding Information
  • Australian Research Council (IL230100039)
  • Australian Research Council (DP220102596)
  • Australian Research Council (DE240100661)
  • Australian Research Council (DE230100471)
  • National Computational Infrastructure
  • Australian Institute of Nuclear Science and Engineering

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