Machine learning-guided discovery of high-efficiency electrolyte additives for aqueous magnesium-air batteries
Yulong Wu, Darya Snihirova, Tim Würger, Linqian Wang, Christian Feiler, Daniel Höche, Sviatlana V. Lamaka, Mikhail L. Zheludkevich
Abstract
Besides alloying, electrolyte additives have emerged as an effective strategy to overcome parasitic anodic hydrogen evolution reactions, and the formation of detrimental deposit layers at Mg-based anodes, thus improving the discharge behavior of Mg-air batteries. However, discovering suitable electrolyte additives through experimental testing is time-consuming and labor-intensive, given their high number of potential candidates. Our recently developed machine learning-based adaptive design was used iteratively in this work. Based on this, electrolyte additive 2,3-dihydroxynaphthalene was discovered, which achieved in a lab-made (Mg-0.2Ca)-air battery a cell voltage of 1.82 V and anodic utilization efficiency of 83%, yielding a specific energy of 3.37k Wh kg −1 . This represents the highest recorded value among all Mg-air batteries reported to date. The results highlight the high potential of machine learning-guided discovery of high-efficiency electrolyte additives to further push the cutting-edge development of high-energy-density Mg-air batteries.