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
1. [1]
  B. Xu, D. Qian, Z. Wang, Y.S. Meng, Mater. Sci. Engin. R: Rep. 73 (2012) 51–65.
2. [2]
  M. Tamaru, S.C. Chung, D. Shimizu, S.I. Nishimura, A. Yamada, Chem. Mater. 25 (2013) 2538–2543. doi: 10.1021/cm4010739
3. [3]
  Y. Tang, Y. Zhang, W. Li, B. Ma, X. Chen, Chem. Soc. Rev. 44 (2015) 5926–5940.
4. [4]
  L. Yang, W. Deng, W. Xu, et al., J. Mater. Chem. A 9 (2021) 14214–14232. doi: 10.1039/d1ta01526e
5. [5]
  K. Kang, Y.S. Meng, J. Breger, C.P. Grey, G. Ceder, Science 311 (2006) 977–980. doi: 10.1126/science.1122152
6. [6]
  P. Zuo, L. Wang, W. Zhang, et al., Nanoscale 7 (2015) 11509–11514.
7. [7]
  D. Choi, J. Xiao, Y.J. Choi, et al., Energy Environ. Sci. 4 (2011) 4560–4566. doi: 10.1039/c1ee01501j
8. [8]
  B. Kang, G. Ceder, Nature 458 (2009) 190–193. doi: 10.1038/nature07853
9. [9]
  P.F. Xiao, B. Ding, M.O. Lai, L. Lu, J. Electrochem. Soc. 160 (2013) A918–A926. doi: 10.1149/2.116306jes
10. [10]
  H. Yang, C. Fu, Y. Sun, L. Wang, T. Liu, Carbon 158 (2020) 102–109.
11. [11]
  J. Li, C. Guo, Y. Qin, X. Ning, Mater. Technol. 35 (2020) 565–571. doi: 10.1080/10667857.2020.1712533
12. [12]
  J. Hong, F. Wang, X. Wang, J. Graetz, J. Power Sources 196 (2011) 3659–3663.
13. [13]
  S. Oukahou, A. Elomrani, M. Maymoun, K. Sbiaai, A. Hasnaoui, Comp. Mater. Sci. 202 (2022) 111006.
14. [14]
  Z. Wei, Q. He, Y. Zhao, J. Power Sources 549 (2022) 232125.
15. [15]
  M. Berecibar, Nature 568 (2019) 325–326. doi: 10.1038/d41586-019-01138-1
16. [16]
  Z. Jiang, J. Li, Y. Yang, et al., Nat. Commun. 11 (2020) 2310.
17. [17]
  L. Himanen, A. Geurts, A.S. Foster, P. Rinke, Adv. Sci. 6 (2019) 1900808.
18. [18]
  J. Schmidt, M.R.G. Marques, S. Botti, M.A.L. Marques, npj Comp. Mater. 5 (2019) 83.
19. [19]
  M. Zhong, K. Tran, Y. Min, et al., Nature 581 (2020) 178–183. doi: 10.1038/s41586-020-2242-8
20. [20]
  A.G. Kusne, H. Yu, C. Wu, H. Zhang, et al., Nat. Commun. 11 (2020) 5966.
21. [21]
  J. Winz, S. Engell, Optimization based sampling for gray-box modeling using a modified upper confidence bound acquisition function, in: 31st European Symposium on Computer Aided Process Engineering, 2021, pp. 953–958.
22. [22]
  F. Pedregosa, G. Varoquaux, A. Gramfort, et al., J. Machine Learning Res. 12 (2011) 2825–2830.
23. [23]
  B.H. Toby, R.B. Von Dreele, J. Appl. Crystal. 46 (2013) 544–549.
24. [24]
  S. Greenhill, S. Rana, S. Gupta, P. Vellanki, S. Venkatesh, IEEE Access 8 (2020) 13937–13948. doi: 10.1109/access.2020.2966228
25. [25]
  G. Kobayashi, A. Yamada, S.I. Nishimura, et al., J. Power Sources 189 (2009) 397–401.
26. [26]
  S.P. Chen, D. Lv, J. Chen, Y.H. Zhang, F.N. Shi, Energy Fuels 36 (2022) 1232–1251. doi: 10.1021/acs.energyfuels.1c03757
27. [27]
  S. Li, H. Zhang, Y. Liu, L. Wang, X. He, Adv. Funct. Mater. 34 (2024) 2310057.
28. [28]
  A. Yamada, Y. Kudo, K.Y. Liu, J. Electrochem. Soc. 148 (2001) A1153.
29. [29]
  D.B. Ravnsbæk, K. Xiang, W. Xing, et al., Nano Lett. 16 (2016) 2375–2380. doi: 10.1021/acs.nanolett.5b05146
30. [30]
  S. Wi, J. Park, S. Lee, et al., Nano Energy 31 (2017) 495–503.
1. [1]
  Yunzhe Zheng , Si Sun , Jiali Liu , Qingyu Zhao , Heng Zhang , Jing Zhang , Peng Zhou , Zhaokun Xiong , Chuan-Shu He , Bo Lai . Application of machine learning for material prediction and design in the environmental remediation. Chinese Chemical Letters, 2025, 36(9): 110722-. doi: 10.1016/j.cclet.2024.110722
2. [2]
  Xiping Dong , Xuan Wang , Zhixiu Lu , Qinhao Shi , Zhengyi Yang , Xuan Yu , Wuliang Feng , Xingli Zou , Yang Liu , Yufeng Zhao . Construction of Cu-Zn Co-doped layered materials for sodium-ion batteries with high cycle stability. Chinese Chemical Letters, 2024, 35(5): 108605-. doi: 10.1016/j.cclet.2023.108605
3. [3]
  Yuting Wu , Haifeng Lv , Xiaojun Wu . Design of two-dimensional porous covalent organic framework semiconductors for visible-light-driven overall water splitting: A theoretical perspective. Chinese Journal of Structural Chemistry, 2024, 43(11): 100375-100375. doi: 10.1016/j.cjsc.2024.100375
4. [4]
  Wenchao Chen , Xiaoting Niu , Xinyu Liang , Guanghao Zhu , Xue Zhang , Qing Gong , Cen Xie , Guangbo Ge , Ping Wang , Yuqi He . Rationally engineered a universal far-red fluorogenic substrate of bile salt hydrolase for functional imaging and inhibitor screening. Chinese Chemical Letters, 2026, 37(4): 111320-. doi: 10.1016/j.cclet.2025.111320
5. [5]
  Honglin Chen , Rupeng Wang , Zixiang He , Shih-Hsin Ho . Data-driven insights into nonradical activation mechanisms for biochar inverse design: A synergistic approach using DFT and machine learning with meta-analysis. Chinese Chemical Letters, 2026, 37(2): 111372-. doi: 10.1016/j.cclet.2025.111372
6. [6]
  Xuan Wang , Peng Sun , Siteng Yuan , Lu Yue , Yufeng Zhao . P2-type low-cost and moisture-stable cathode for sodium-ion batteries. Chinese Chemical Letters, 2025, 36(5): 110015-. doi: 10.1016/j.cclet.2024.110015
7. [7]
  Zonglin Li , Shihua Zou , Zining Wang , Georgeta Postole , Liang Hu , Hongying Zhao . Machine learning in electrochemical oxidation process: A mini-review. Chinese Chemical Letters, 2025, 36(8): 110526-. doi: 10.1016/j.cclet.2024.110526
8. [8]
  Zixing Xu , Ruiying Chen , Chuanming Hao , Qionghong Xie , Chunhui Deng , Nianrong Sun . Peptidome data-driven comprehensive individualized monitoring of membranous nephropathy with machine learning. Chinese Chemical Letters, 2024, 35(5): 108975-. doi: 10.1016/j.cclet.2023.108975
9. [9]
  Lu Li , Jianing Shen , Qinkun Xiao , Chaozheng He , Jinzhou Zheng , Chaoqin Chu , Chen Chen . Stable crystal structure prediction using machine learning-based formation energy and empirical potential function. Chinese Chemical Letters, 2025, 36(11): 110421-. doi: 10.1016/j.cclet.2024.110421
10. [10]
  Han-Bin Liu , Xiaoyu Cheng , Zhou Guo , Juan Yang , Fuwen Tan , Donghui Lan , Jian-Ping Tan , Bing Yi , Weixin Zhai , Qing-Hui Guo . CrownBind-IA: A machine learning model predicting binding constants between crown ethers and alkali metal ions. Chinese Chemical Letters, 2025, 36(12): 111149-. doi: 10.1016/j.cclet.2025.111149
11. [11]
  Xu He , Wenjie Gao , Jinglei Xu , Zhanjun Cheng , Wenchao Peng , Beibei Yan , Guanyi Chen , Ning Li . Machine learning-assisted construction of C=O and pyridinic N active sites in sludge-based catalysts. Chinese Chemical Letters, 2025, 36(12): 111019-. doi: 10.1016/j.cclet.2025.111019
12. [12]
  Shuang Li , Penghui Yuan , Xinyi Zhang , Meiru Liu , Dezhi Yang , Linglei Kong , Li Zhang , Yang Lu , Guanhua Du . Revolutionizing sepsis therapy: Machine learning-driven co-crystallization reveals emodin's therapeutic potential. Chinese Chemical Letters, 2026, 37(2): 111289-. doi: 10.1016/j.cclet.2025.111289
13. [13]
  Na Qin , Wenxin Guo , Fangxiu Li , Houfeng Zhang , Hong Liu , Chang Zhang , Lipiao Bao , Lei Liu , Muneerah Alomar , Siqi Zhao , Jian Zhang , Xing Lu . Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction. Chinese Chemical Letters, 2026, 37(3): 112021-. doi: 10.1016/j.cclet.2025.112021
14. [14]
  Dongshi Feng , Jiangdong Dai , Zhi Zhu , Pengwei Huo , Yongsheng Yan , Chunxiang Li . Molecularly imprinted electrochemical sensor arrays combined with machine learning for simultaneous determination of three neonicotinoid insecticides. Chinese Chemical Letters, 2026, 37(5): 111789-. doi: 10.1016/j.cclet.2025.111789
15. [15]
  Jieyu Liu , Junze Zhang , Haigang Deng , Shuoao Wang , Xingxing Jiang , Li Wang , Changhong Wang . Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH₃ conversion by DFT studies and machine learning. Chinese Chemical Letters, 2025, 36(12): 110656-. doi: 10.1016/j.cclet.2024.110656
16. [16]
  Hao Chen , Haiyuan Liao , Qi Zhou , Yang Liu , Guojun Liu , Yuan Yao . Electronegativity-oriented coordination regulation of main-group metal single-atom catalysts for oxygen reduction to H₂O₂: A combined study of first-principles and machine learning. Chinese Chemical Letters, 2026, 37(3): 110711-. doi: 10.1016/j.cclet.2024.110711
17. [17]
  Xiangyue Li , Dexin Zhu , Kunmin Pan , Xiaoye Zhou , Jiaming Zhu , Yingxue Wang , Yongpeng Ren , Hong-Hui Wu . Identifying key determinants of discharge capacity in ternary cathode materials of lithium-ion batteries. Chinese Chemical Letters, 2025, 36(5): 109870-. doi: 10.1016/j.cclet.2024.109870
18. [18]
  Xi Xue , Kai Chen , Hanyu Sun , Xiangying Liu , Xue Liu , Shize Li , Jingjie Yan , Yu Peng , Mohammad S. Mubarak , Ahmed Al-Harrasi , Hai-Yu Hu , Yafeng Deng , Xiandao Pan , Xiaojian Wang . Bidirectional Chemical Intelligent Net: A unified deep learning–based framework for predicting chemical reaction. Chinese Chemical Letters, 2025, 36(11): 110968-. doi: 10.1016/j.cclet.2025.110968
19. [19]
  Chengde Wang , Liping Huang , Shanshan Wang , Lihao Wu , Yi Wang , Jun Dong . A distinction of gliomas at cellular and tissue level by surface-enhanced Raman scattering spectroscopy. Chinese Chemical Letters, 2024, 35(5): 109383-. doi: 10.1016/j.cclet.2023.109383
20. [20]
  Xiaoxiao Wang , Bolun Wang , Fenfen Ji , Jie Yan , Jiacheng Fang , Doudou Zhang , Ji Xu , Jing Ji , Xinran Hao , Hemi Luan , Yanjun Hong , Shulan Qiu , Min Li , Zhu Yang , Wenlan Liu , Xiaodong Cai , Zongwei Cai . Discovery of plasma biomarkers for Parkinson’s disease diagnoses based on metabolomics and lipidomics. Chinese Chemical Letters, 2024, 35(11): 109653-. doi: 10.1016/j.cclet.2024.109653

Get Citation

PDF

XML

Metrics

PDF Downloads(7)
Abstract views(1700)
HTML views(93)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

High-throughput screening of high energy density LiMn1-xFexPO4 via active learning

Metrics

通讯作者: 陈斌, bchen63@163.com

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