Data-driven selection of electrolyte additives for aqueous magnesium batteries,Journal of Materials Chemistry A

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Data-driven selection of electrolyte additives for aqueous magnesium batteries
Journal of Materials Chemistry A ( IF 10.7 ) Pub Date : 2022-09-23 , DOI: 10.1039/d2ta04538a
Tim Würger _{1,

2} , Linqian Wang ₁ , Darya Snihirova ₁ , Min Deng ₁ , Sviatlana V. Lamaka ₁ , David A. Winkler _{3,

4,

5} , Daniel Höche ₁ , Mikhail L. Zheludkevich _{1,

6} , Robert H. Meißner _{1,

2} , Christian Feiler ₁

Affiliation

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.

中文翻译：

用于水性镁电池的电解质添加剂的数据驱动选择

水性一次镁空气电池作为海洋应用和便携式设备的能源具有相当大的潜力。然而，必须解决与自腐蚀、电解质老化和块状效应相关的负极-电解质界面的一些挑战，以提高电池的放电电位以及负极材料的利用率。除了合金化，减轻自腐蚀和电池故障的有效策略是使用电解质添加剂。从可能化合物的巨大化学空间中选择有用的添加剂并非易事。幸运的是，数据驱动的定量结构-性能关系 (QSPR) 模型可以促进有效搜索有前途的电池助推器候选者。这里，使用积极的实验设计方法评估了两个 QSPR 模型的稳健性和预测性能。我们还提出了一种多目标优化方法，该方法允许识别新的电解质添加剂，这些添加剂可以提高电池阳极相对于目标应用的性能，从而加速先进电池系统的发现。

更新日期：2022-09-23

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