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.　©　2024　Elsevier　B.V.