Citation: Qingyun Hu, Wei Wang, Junyuan Lu, He Zhu, Qi Liu, Yang Ren, Hong Wang, Jian Hui. High-throughput screening of high energy density LiMn_1-xFe_xPO₄ via active learning[J]. Chinese Chemical Letters, ;2025, 36(2): 110344. doi: 10.1016/j.cclet.2024.110344 shu

High-throughput screening of high energy density LiMn_1-xFe_xPO₄ via active learning

Qingyun Hu ^{a, b} ,
Wei Wang ^c ,
Junyuan Lu ^a ,
He Zhu ^d ,
Qi Liu ^{c, e, *,} ,
Yang Ren ^c ,
Hong Wang ^{a, b} ,
Jian Hui ^{a, b, *,}

a.
School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
b.
Zhang Jiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 201210, China
c.
Department of Physics, City University of Hong Kong, Hong Kong 999077, China
d.
Herbert Gleiter Institute of Nanoscience, School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
e.
Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China

qiliu63@cityu.edu.hk

hj20151107@sjtu.edu.cn

Received Date: 18 May 2024
Revised Date: 5 July 2024
Accepted Date: 16 August 2024
Available Online: 17 August 2024

Figures(5)

Lithium-ion batteries (LiBs) with high energy density have gained significant popularity in smart grids and portable electronics. LiMn_1-xFe_xPO₄ (LMFP) is considered a leading candidate for the cathode, with the potential to combine the low cost of LiFePO₄ (LFP) with the high theoretical energy density of LiMnPO₄ (LMP). However, quantitative investigation of the intricate coupling between the Fe/Mn ratio and the resulting energy density is challenging due to the parametric complexity. It is crucial to develop a universal approach for the rapid construction of multi-parameter mapping. In this work, we propose an active learning-guided high-throughput workflow for quantitatively predicting the Fe/Mn ratio and the energy density mapping of LMFP. An optimal composition (LiMn_0.66Fe_0.34PO₄) was effectively screened from 81 cathode materials via only 5 samples. Model-guided electrochemical analysis revealed a nonlinear relationship between the Fe/Mn ratio and electrochemical properties, including ion mobility and impedance, elucidating the quantitative chemical composition-energy density map of LMFP. The results demonstrated the efficacy of the method in high-throughput screening of LiBs cathode materials.
- High-throughput screening,
- Machine learning,
- Cathode material,
- Performance optimization,
- Quantitative map
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High-throughput screening of high energy density LiMn_1-xFe_xPO₄ via active learning