硅酸盐学报

机器学习驱动的富锂正极活化条件优化

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战晨 ¹^,³ , 刘方正 ¹^,³ , 李裕铭 ¹^,³ , 李诚瀚 ¹^,³ , 邹佩 ¹^,³ , 李想 ¹^,² , 张旭 ¹^,²^,³ , 郭现伟 ¹^,³ , 赵姝 ¹^,³ , 尉海军 ¹^,²^,³

硅酸盐学报 | AI for Materials专题—研究论文 2026,54(1): 3-13

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硅酸盐学报 |AI for Materials专题—研究论文 2026 , 54 (1) : 3 -13

机器学习驱动的富锂正极活化条件优化

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战晨¹^,³, 刘方正¹^,³, 李裕铭¹^,³, 李诚瀚¹^,³, 邹佩¹^,³, 李想¹^,², 张旭¹^,²^,³, 郭现伟¹^,³, 赵姝¹^,³, 尉海军¹^,²^,³

作者信息

^1．北京工业大学材料科学与工程学院先进电池材料与器件研究所，北京 100124

^2．工大国创先进电池材料与器件研究院，北京 100176

^3．北京工业大学材料循环低碳再生全国重点实验室，新型功能材料教育部重点实验室，北京 100124

通讯作者:

赵姝（1987-），女，博士，副研究员

尉海军（1980-），男，博士，教授。

作者简介:

战晨（2001-），男，硕士研究生。

Machine Learning-Driven Optimization of Activation Conditions for Lithium-Rich Cathode Materials

Chen ZHAN¹^,³, Fangzheng LIU¹^,³, Yuming LI¹^,³, Chenghan LI¹^,³, Pei ZOU¹^,³, Xiang LI¹^,², Xu ZHANG¹^,²^,³, Xianwei GUO¹^,³, Shu ZHAO¹^,³, Haijun YU¹^,²^,³

Affiliations

^1.Institute of Advanced Battery Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China

^2.Institute of Gongda-Guochuang, Advanced Battery Materials and Devices, Beijing 100176, China

^3.State Key Laboratory of Materials Low-Carbon Recycling, Key Laboratory of Advanced Functional Materials, Ministry of Education, Beijing University of Technology, Beijing 100124, China

Corresponding Author:

ZHAO Shu (1987-), female, Ph.D., Associate Professor; E-mail: zhaoshu@bjut.edu.cn

YU Haijun (1980-), male, Ph.D., Professor. E-mail: hj-yu@bjut.edu.cn

ZHAN Chen (2001-), male, Master candidate. E-mail: zc13611104348@126.com

出版时间: 2026-01-20 doi: 10.14062/j.issn.0454-5648.20250819

文章导航

摘要

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富锂锰基层状氧化物因其低成本和高可逆容量，被认为是最具潜力的下一代锂离子电池正极材料。然而其电化学性能对初始活化条件高度敏感，受电压、温度、电流密度及活化圈数等多因素耦合影响。为实现活化制度的高效优化，本工作构建了基于机器学习的活化参数优化框架。通过整合实验与文献数据建立多特征数据集，并以CatBoost模型为核心构建容量保持率预测体系，实现了对不同活化条件下电池循环性能的高精度预测。进一步结合贝叶斯优化，在高维参数空间中实现多参数协同寻优，所得最优活化条件下模型预测结果与实验结果的容量保持率误差低于1%。本工作实现了富锂正极电化学活化工艺的智能优化，为复杂电化学体系的多参数定量调控与高性能正极材料设计提供了新的思路与可行路径。

关键词

富锂锰基层状氧化物 / 机器学习 / 贝叶斯优化 / 电化学活化 / 锂离子电池

Abstract

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Introduction

Lithium-rich manganese-based layered oxides (LLOs) are considered as next-generation promising cathode materials for lithium-ion batteries due to the high theoretical energy density (exceeding 1000 W·h·kg^-1) and low cost. However, their electrochemical performance is highly sensitive to the initial activation conditions, including initial charge cut off voltage, activation temperature, initial current density, and activation cycle number. The activation process involves the electrochemical transformation of Li₂MnO₃ domains into active phases, which is crucial for achieving high capacity but leads to irreversible oxygen release and structural degradation. Traditional optimization methods rely on single-factor experiments, which are inefficient and fail to address the complex coupling effects of multiple parameters. This study proposes a machine learning driven framework to optimize the activation conditions of LLOs, addressing the limitations of conventional approaches by enabling high-dimensional parameter search and predictive modeling. This work highlights the necessity of artificial intelligent optimization for enhancing the stability and performance of high-energy-density batteries.

Methods

The research constructed a dataset comprising nearly 100 samples from laboratory experiments and research articles, featuring key parameters such as Li₂MnO₃ domain ratio (x = 0.1-0.5), activation charge cutoff voltage (4.4-4.8 V), activation temperature (25-55 ℃), activation current density (20-220 mA·g^-1), and activation cycles (1-4). Data preprocessing included unit standardization and mean imputation for missing values. Six supervised learning regression models—Lasso Regression, Ridge Regression, Support Vector Regression (SVR), Decision Tree Regression (DTR), XGBoost, and CatBoost—were trained to predict the 50th-cycle capacity retention. Hyperparameter optimization was performed using grid search with 5-fold cross-validation, evaluated by metrics like R², MAE, MSE, and MAPE. The CatBoost model demonstrated superior prediction performance and was selected for further analysis. Feature importance was interpreted using SHapley Additive exPlanations (SHAP), and Bayesian optimization with Gaussian process regression was applied to identify optimal activation conditions by maximizing capacity retention. Experimental validation involved synthesizing LLOs with different Li₂MnO₃ ratios (e.g., 0.3Li₂MnO₃·0.7LiMn_0.42Ni_0.42Co_0.16O₂ and 0.5Li₂MnO₃·0.5LiMn_2.42Ni_0.42Co_2.16O₂), characterized by XRD and electrochemical performance testing.

Results and Discussion

The CatBoost model achieved the highest prediction accuracy (R² = 0.777, MSE = 3.00 × 10^-4), significantly outperforming linear models and other nonlinear algorithms. SHAP analysis revealed that Li₂MnO₃ domain ratio and activation charging cutoff voltage were the most influential features, with high voltages (>4.6 V) and high Li₂MnO₃ content negatively impacting capacity retention due to accelerated oxygen loss and structural degradation. Bayesian optimization identified distinct optimal conditions for different LLOs compositions: for a Li₂MnO₃ ratio of 0.3, the optimal parameters were 4.49 V charging cutoff voltage, 25 ℃ activation temperature, and 20 mA·g^-1 activation current density, yielding lowest capacity decay; for a ratio of 0.5, a higher temperature of 43.95 ℃ was required to fully activate the material while maintaining stability. Experimental validation confirmed the model's predictions, with capacity retention errors below 1% compared to experimental result. For instance, under optimized conditions, the 3/7 LLOs sample maintained 100.9% capacity retention after 50 cycles, versus 90.6% with conventional high-voltage activation. The results demonstrate that ML enables precise control of multi-parameter interactions, balancing capacity and stability by suppressing irreversible reactions.

Conclusions

This study successfully integrates machine learning with experimental research to optimize the electrochemical activation of lithium-rich cathodes. The CatBoost-based predictive model accurately captures nonlinear relationships between activation parameters and capacity retention, while Bayesian optimization efficiently identifies global optimal conditions in high-dimensional space. The work provides a data-driven strategy for designing high-performance LLOs, emphasizing the critical roles of voltage and temperature in structural evolution. This approach significantly reduces experimental costs and time, offering a generalizable framework for multi-parameter optimization in complex electrochemical systems. Future applications could extend to other battery materials and processes, advancing the development of intelligent materials design.

Key words

lithium-rich manganese layered oxides / machine learning / bayesian optimization / electrochemical activation / lithium-ion batteries

引用本文

战晨, 刘方正, 李裕铭, 李诚瀚, 邹佩, 李想, 张旭, 郭现伟, 赵姝, 尉海军. 机器学习驱动的富锂正极活化条件优化. 硅酸盐学报, 2026 , 54 (1) : 3 -13 . DOI: 10.14062/j.issn.0454-5648.20250819

Chen ZHAN, Fangzheng LIU, Yuming LI, Chenghan LI, Pei ZOU, Xiang LI, Xu ZHANG, Xianwei GUO, Shu ZHAO, Haijun YU. Machine Learning-Driven Optimization of Activation Conditions for Lithium-Rich Cathode Materials[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (1) : 3 -13 . DOI: 10.14062/j.issn.0454-5648.20250819

基金

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国家重点研发计划资助项目(2022YFB2404400)
国家自然科学基金资助项目(92263206; 92572203; 52372168; U23A20577; U24A20549)

参考文献

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排序方式：

2026年第54卷第1期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250819

接收时间：2025-11-07
首发时间：2026-10-27
出版时间：2026-01-20

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出版历史

收稿日期：2025-11-07
修回日期：2025-12-11

基金

国家重点研发计划资助项目(2022YFB2404400)

国家自然科学基金资助项目(92263206; 92572203; 52372168; U23A20577; U24A20549)

作者信息

^1．北京工业大学材料科学与工程学院先进电池材料与器件研究所，北京 100124

^2．工大国创先进电池材料与器件研究院，北京 100176

^3．北京工业大学材料循环低碳再生全国重点实验室，新型功能材料教育部重点实验室，北京 100124

通讯作者:

赵姝（1987-），女，博士，副研究员

尉海军（1980-），男，博士，教授。

参考文献

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250819

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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