Data-driven selection of electrolyte additives for aqueous magnesium batteries

Aqueous primary Mg-air batteries have considerable potential as energy sources for sea applications and portable devices. However, some challenges at the anode-electrolyte interface related to self-corrosion, aging of the electrolyte and the chunk-effect have to be solved to improve the discharge potential of the battery as well as the utilization efficiency of the anode material. Aside from alloying, an effective strategy to mitigate self-corrosion and battery failure is the use of electrolyte additives. Selecting useful additives from the vast chemical space of possible compounds is not a trivial task. Fortunately, data-driven quantitative structure-property relationship (QSPR) models can facilitate efficient searches for promising battery booster candidates. Here, the robustness and predictive performance of two QSPR models are evaluated using an active design of experiments approach. We also present a multi-objective optimization method that allows to identify new electrolyte additives that can boost the battery anode performance with respect to a target application, thus accelerating the discovery of advanced battery systems.

Automated summary

This study evaluates the robustness and predictive performance of two QSPR models and proposes a multi-objective optimization method to accelerate the discovery of advanced battery systems.