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Na[Mn<sub>0.36</sub>Ni<sub>0.44</sub>Ti<sub>0.15</sub>Fe<sub>0.05</sub>]O<sub>2</sub> predicted <i>via</i> machine learning for high energy Na-ion batteries

Saaya Sekine, Tomooki Hosaka, Hayato Maejima, Ryoichi Tatara, Masanobu Nakayama, Shinichi Komaba

2024Journal of Materials Chemistry A14 citationsDOIOpen Access PDF

Abstract

We optimize the composition of transition metal layered oxides for high energy Na-ion batteries using machine learning trained by our experimental data.

Topics & Concepts

Materials scienceAdvancements in Battery MaterialsAdvanced Battery Technologies ResearchExtraction and Separation Processes
Na[Mn<sub>0.36</sub>Ni<sub>0.44</sub>Ti<sub>0.15</sub>Fe<sub>0.05</sub>]O<sub>2</sub> predicted <i>via</i> machine learning for high energy Na-ion batteries | Litcius