通过机器学习预测 Na[Mn0.36Ni0.44Ti0.15Fe0.05]O2，用于高能量钠离子电池

IF 10.7 2区材料科学 Q1 CHEMISTRY, PHYSICAL

Journal of Materials Chemistry A Pub Date : 2024-09-05 DOI:10.1039/D4TA04809A

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

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引用次数: 0

摘要

我们利用由实验数据训练的机器学习（ML）优化了用于高能纳离子电池的过渡金属层状氧化物的组成。ML 模型预测了它们的电化学性能，并提出了很有前景的四元 Na[Ni,Mn,Fe,Ti]O2 成分。因此，我们合成了 Na[Mn0.36Ni0.44Ti0.15Fe0.05]O2，其能量密度高达 549 Wh kg-1，与预测值一致。

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

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

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.

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来源期刊

Journal of Materials Chemistry A CHEMISTRY, PHYSICAL-ENERGY & FUELS

CiteScore

19.50

自引率

5.00%

发文量

1892

审稿时长

1.5 months

期刊介绍： The Journal of Materials Chemistry A, B & C covers a wide range of high-quality studies in the field of materials chemistry, with each section focusing on specific applications of the materials studied. Journal of Materials Chemistry A emphasizes applications in energy and sustainability, including topics such as artificial photosynthesis, batteries, and fuel cells. Journal of Materials Chemistry B focuses on applications in biology and medicine, while Journal of Materials Chemistry C covers applications in optical, magnetic, and electronic devices. Example topic areas within the scope of Journal of Materials Chemistry A include catalysis, green/sustainable materials, sensors, and water treatment, among others.