Na[Mn0.36Ni0.44Ti0.15Fe0.05]O2 predicted via machine learning for high energy Na-ion batteries

Abstract

We optimize the composition of transition metal layered oxides for high energy Na-ion batteries using machine learning (ML) trained by our experimental data. The ML models predict their electrochemical performance and suggest promising compositions of quaternary Na[Ni,Mn,Fe,Ti]O₂. Accordingly, we synthesized Na[Mn_0.36Ni_0.44Ti_0.15Fe_0.05]O₂ which achieves a high energy density of 549 W h per kg of active material, agreeing with the predicted value.