Evading the efficiency-voltage trade-off in magnesium air battery via a novel active learning framework

Reconciling the inherent trade-off between anodic efficiency and discharge voltage in the design of high-energy-density magnesium-air (Mg-air) batteries has been a persistent challenge. Herein, we propose a pioneering active learning strategy that integrates physically motivated variables, machine learning, exploration of the Pareto front, experimental data feedback, and generated data feedback for the purpose of designing magnesium anodes. Within an extensive compositional space (∼350,000 possibilities), we have pinpointed a novel alloy, Mg-1Ga-1Ca-0.5In, exhibiting exceptional performance with high efficiency (64 ± 5.5 % at 1 mA cm−2, 64 ± 0.5 % at 10 mA cm−2) and high voltage (1.80 ± 0.00 V at 1 mA cm−2, 1.57 ± 0.01 V at 10 mA cm−2), surpassing conventional methods of alloying. Subsequent experiments and density functional theory (DFT) calculations have unveiled that the outstanding performance of Mg-1Ga-1Ca-0.5In stems from “grain boundary activation” induced by active second phases and “intra-grain inhibition” resulting from the orbital hybridization between solute atoms and Mg atoms. This study provides a novel research paradigm and offers valuable insights for the further development of high-performance Mg-air batteries.