Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach
Doping lithium cobalt oxide (LiCoO<sub>2</sub>) cathode materials is an effective strategy for mitigating the detrimental phase transitions that occur at high voltages. A deep understanding of the relationships between cycle capacity and the design elements of doped LiCoO<sub>2<...
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2025-03-01
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| author | Man Fang Yutong Yao Chao Pang Xiehang Chen Yutao Wei Fan Zhou Xiaokun Zhang Yong Xiang |
| author_facet | Man Fang Yutong Yao Chao Pang Xiehang Chen Yutao Wei Fan Zhou Xiaokun Zhang Yong Xiang |
| author_sort | Man Fang |
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| description | Doping lithium cobalt oxide (LiCoO<sub>2</sub>) cathode materials is an effective strategy for mitigating the detrimental phase transitions that occur at high voltages. A deep understanding of the relationships between cycle capacity and the design elements of doped LiCoO<sub>2</sub> is critical for overcoming the existing research limitations. The key lies in constructing a robust and interpretable mapping model between data and performance. In this study, we analyze the correlations between the features and cycle capacity of 158 different element-doped LiCoO<sub>2</sub> systems by using five advanced machine learning algorithms. First, we conducted a feature election to reduce model overfitting through a combined approach of mechanistic analysis and Pearson correlation analysis. Second, the experimental results revealed that RF and XGBoost are the two best-performing models for data fitting. Specifically, the RF and XGBoost models have the highest fitting performance for IC and EC prediction, with R<sup>2</sup> values of 0.8882 and 0.8318, respectively. Experiments focusing on ion electronegativity design verified the effectiveness of the optimal combined model. We demonstrate the benefits of machine learning models in uncovering the core elements of complex doped LiCoO<sub>2</sub> formulation design. Furthermore, these combined models can be employed to search for materials with superior electrochemical performance and processing conditions. In the future, we aim to develop more accurate and efficient machine learning algorithms to explore the microscopic mechanisms affecting doped layered oxide cathode material design, thereby establishing new paradigms for the research of high-performance cathode materials for lithium batteries. |
| format | Article |
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| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Batteries |
| spelling | doaj-art-63e77f37727b4e2ca823eabf05675ba82025-08-20T03:43:01ZengMDPI AGBatteries2313-01052025-03-0111310010.3390/batteries11030100Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven ApproachMan Fang0Yutong Yao1Chao Pang2Xiehang Chen3Yutao Wei4Fan Zhou5Xiaokun Zhang6Yong Xiang7School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610056, ChinaSchool of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610056, ChinaDoping lithium cobalt oxide (LiCoO<sub>2</sub>) cathode materials is an effective strategy for mitigating the detrimental phase transitions that occur at high voltages. A deep understanding of the relationships between cycle capacity and the design elements of doped LiCoO<sub>2</sub> is critical for overcoming the existing research limitations. The key lies in constructing a robust and interpretable mapping model between data and performance. In this study, we analyze the correlations between the features and cycle capacity of 158 different element-doped LiCoO<sub>2</sub> systems by using five advanced machine learning algorithms. First, we conducted a feature election to reduce model overfitting through a combined approach of mechanistic analysis and Pearson correlation analysis. Second, the experimental results revealed that RF and XGBoost are the two best-performing models for data fitting. Specifically, the RF and XGBoost models have the highest fitting performance for IC and EC prediction, with R<sup>2</sup> values of 0.8882 and 0.8318, respectively. Experiments focusing on ion electronegativity design verified the effectiveness of the optimal combined model. We demonstrate the benefits of machine learning models in uncovering the core elements of complex doped LiCoO<sub>2</sub> formulation design. Furthermore, these combined models can be employed to search for materials with superior electrochemical performance and processing conditions. In the future, we aim to develop more accurate and efficient machine learning algorithms to explore the microscopic mechanisms affecting doped layered oxide cathode material design, thereby establishing new paradigms for the research of high-performance cathode materials for lithium batteries.https://www.mdpi.com/2313-0105/11/3/100high voltageLCOdopingmachine learningdata-driven |
| spellingShingle | Man Fang Yutong Yao Chao Pang Xiehang Chen Yutao Wei Fan Zhou Xiaokun Zhang Yong Xiang Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach Batteries high voltage LCO doping machine learning data-driven |
| title | Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach |
| title_full | Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach |
| title_fullStr | Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach |
| title_full_unstemmed | Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach |
| title_short | Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO<sub>2</sub>: A Data-Driven Approach |
| title_sort | machine learning assisted design of doping strategies for high voltage licoo sub 2 sub a data driven approach |
| topic | high voltage LCO doping machine learning data-driven |
| url | https://www.mdpi.com/2313-0105/11/3/100 |
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