Citation: Yong Qiu, Xu Zhang, Yun Tian, Zhen Zhou. Machine learning promotes the development of all-solid-state batteries[J]. Chinese Journal of Structural Chemistry, ;2023, 42(9): 100118. doi: 10.1016/j.cjsc.2023.100118 shu

Machine learning promotes the development of all-solid-state batteries

Lithium-ion batteries (LIBs) are a promising energy storage system for green energy applications. However, the use of liquid electrolytes in LIBs results in safety and lifespan issues. To address these challenges, researchers have been focusing on the development of all-solid-state batteries that use solid electrolytes. Unfortunately, traditional methods are time-consuming and expensive for exploring solid-state batteries, limiting their ability to keep up with growing social demand. In recent years, the development of big data has opened up new avenues for materials discovery, allowing for large-scale materials screening through computer simulations and machine learning models that can disclose the structure-activity relationship of materials. This review provides an overview of the basic procedures and common algorithms used in machine learning for designing solid-state batteries, with particular emphasis on recent research progress in applying machine learning to cathode materials and solid electrolytes, as well as predicting the condition of solid-state batteries. Additionally, this review offers a brief outlook on the challenges and opportunities facing machine learning methods in the realm of solid-state batteries.
- Machine learning,
- Solid-state batteries,
- Performance prediction,
- Big data
1. [1]
  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
2. [2]
  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
3. [3]
  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. Chinese Chemical Letters, 2025, 36(2): 110344-. doi: 10.1016/j.cclet.2024.110344
4. [4]
  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
5. [5]
  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
6. [6]
  Ze Zhang , Lei Yang , Jin-Ru Liu , Hao Hu , Jian-Li Mi , Chao Su , Bei-Bei Xiao , Zhi-Min Ao . Improved oxygen electrocatalysis at FeN₄ and CoN₄ sites via construction of axial coordination. Chinese Chemical Letters, 2025, 36(2): 110013-. doi: 10.1016/j.cclet.2024.110013
7. [7]
  Ziling Jiang , Shaoqing Chen , Chaochao Wei , Ziqi Zhang , Zhongkai Wu , Qiyue Luo , Liang Ming , Long Zhang , Chuang Yu . Enabling superior electrochemical performance of NCA cathode in Li_5.5PS_4.5Cl_1.5-based solid-state batteries with a dual-electrolyte layer. Chinese Chemical Letters, 2024, 35(4): 108561-. doi: 10.1016/j.cclet.2023.108561
8. [8]
  Tianyi Hou , Yunhui Huang , Henghui Xu . Interfacial engineering for advanced solid-state Li-metal batteries. Chinese Journal of Structural Chemistry, 2024, 43(7): 100313-100313. doi: 10.1016/j.cjsc.2024.100313
9. [9]
  Liang Ming , Dan Liu , Qiyue Luo , Chaochao Wei , Chen Liu , Ziling Jiang , Zhongkai Wu , Lin Li , Long Zhang , Shijie Cheng , Chuang Yu . Si-doped Li₆PS₅I with enhanced conductivity enables superior performance for all-solid-state lithium batteries. Chinese Chemical Letters, 2024, 35(10): 109387-. doi: 10.1016/j.cclet.2023.109387
10. [10]
  Qianqian Song , Yunting Zhang , Jianli Liang , Si Liu , Jian Zhu , Xingbin Yan . Boron nitride nanofibers enhanced composite PEO-based solid-state polymer electrolytes for lithium metal batteries. Chinese Chemical Letters, 2024, 35(6): 108797-. doi: 10.1016/j.cclet.2023.108797
11. [11]
  Ying Li , Yanjun Xu , Xingqi Han , Di Han , Xuesong Wu , Xinlong Wang , Zhongmin Su . A new metal–organic rotaxane framework for enhanced ion conductivity of solid-state electrolyte in lithium-metal batteries. Chinese Chemical Letters, 2024, 35(9): 109189-. doi: 10.1016/j.cclet.2023.109189
12. [12]
  Yang Deng , Yitao Ouyang , Chao Han . Constriction-susceptible makes fast cycling of lithium metal in solid-state batteries: Silicon as an example. Chinese Journal of Structural Chemistry, 2024, 43(7): 100276-100276. doi: 10.1016/j.cjsc.2024.100276
13. [13]
  Yiwen Lin , Yijie Chen , Chunhui Deng , Nianrong Sun . Integration of resol/block-copolymer carbonization and machine learning: A convenient approach for precise monitoring of glycan-associated disorders. Chinese Chemical Letters, 2024, 35(12): 109813-. doi: 10.1016/j.cclet.2024.109813
14. [14]
  Biao Fang , Runwei Mo . PVDF-based solid-state battery. Chinese Journal of Structural Chemistry, 2024, 43(8): 100347-100347. doi: 10.1016/j.cjsc.2024.100347
15. [15]
  Xin Li , Ling Zhang , Yunyan Fan , Shaojing Lin , Yong Lin , Yongsheng Ying , Meijiao Hu , Haiying Gao , Xianri Xu , Zhongbiao Xia , Xinchuan Lin , Junjie Lu , Xiang Han . Carbon interconnected microsized Si film toward high energy room temperature solid-state lithium-ion batteries. Chinese Chemical Letters, 2025, 36(2): 109776-. doi: 10.1016/j.cclet.2024.109776
16. [16]
  Yao-Hua Gu , Yu Chen , Qing Li , Neng-Bin Xie , Xue Xing , Jun Xiong , Min Hu , Tian-Zhou Li , Ke-Yu Yuan , Yu Liu , Tang Tang , Fan He , Bi-Feng Yuan . Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease. Chinese Chemical Letters, 2024, 35(11): 109627-. doi: 10.1016/j.cclet.2024.109627
17. [17]
  Xinzhi Ding , Chong Liu , Jing Niu , Nan Chen , Shutao Xu , Yingxu Wei , Zhongmin Liu . Solid-state NMR study of the stability of MOR framework aluminum. Chinese Journal of Structural Chemistry, 2024, 43(4): 100247-100247. doi: 10.1016/j.cjsc.2024.100247
18. [18]
  Xiaochen Zhang , Fei Yu , Jie Ma . 多角度数理模拟在电容去离子中的前沿应用. Acta Physico-Chimica Sinica, 2024, 40(11): 2311026-. doi: 10.3866/PKU.WHXB202311026
19. [19]
  Jia Zhou . Constructing Potential Energy Surface of Water Molecule by Quantum Chemistry and Machine Learning: Introduction to a Comprehensive Computational Chemistry Experiment. University Chemistry, 2024, 39(3): 351-358. doi: 10.3866/PKU.DXHX202309060
20. [20]
  Xinghai Li , Zhisen Wu , Lijing Zhang , Shengyang Tao . Machine Learning Enables the Prediction of Amide Bond Synthesis Based on Small Datasets. Acta Physico-Chimica Sinica, 2025, 41(2): 100010-. doi: 10.3866/PKU.WHXB202309041

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