物理化学学报 >> 2026, Vol. 42 >> Issue (7): 100232.doi: 10.1016/j.actphy.2025.100232

所属专题： 二次电池先进正极材料

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一种数据驱动方法快速筛选高性能水系锌离子电池中金属掺杂二氧化锰正极

单昱呈1, 许立明3, 孙鹏2, 朱之婧4, 王成龙1,*(), 黎晋良2,*(), 杨光1, 潘丽坤1,*()   

  1. 1 华东师范大学物理学院, 上海磁共振重点实验室, 医学磁共振与分子影像技术研究院, 上海 200241
    2 暨南大学物理与光电工程学院物理系, 广东省真空镀膜技术与新能源材料工程技术研究中心, 思源实验室, 广东 广州 510632
    3 江西科技师范大学, 柔性电子江西省重点实验室, 江西 南昌 330013
    4 上海理工大学材料与化学学院, 上海 200093
  • 收稿日期:2025-10-01 修回日期:2025-12-02 录用日期:2025-12-17 发布日期:2026-05-22
  • 通讯作者: Email: clwang@phy.ecnu.edu.cn (王成龙)lijinliang@email.jnu.edu.cn (黎晋良)lkpan@phy.ecnu.edu.cn (潘丽坤)

A data-driven approach for rapid revealing of metal doping in MnO2 cathodes for high-performance aqueous zinc-ion batteries

Yucheng Shan1, Liming Xu3, Peng Sun2, Zhijing Zhu4, Chenglong Wang1,*(), Jinliang Li2,*(), Guang Yang1, Likun Pan1,*()   

  1. 1 Shanghai Key Laboratory of Magnetic Resonance, School of Physics, Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai 200241, China
    2 Siyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, Department of Physics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, Guangdong Province, China
    3 Jiangxi Provincial Key Laboratory of Flexible Electronics, Jiangxi Science and Technology Normal University, Nanchang 330013, Jiangxi Province, China
    4 School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China
  • Received:2025-10-01 Revised:2025-12-02 Accepted:2025-12-17 Published:2026-05-22
  • Contact: Email: clwang@phy.ecnu.edu.cn (Chenglong Wang)lijinliang@email.jnu.edu.cn (Jinliang Li)lkpan@phy.ecnu.edu.cn (Likun Pan)

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摘要：

金属掺杂是水系锌离子电池二氧化锰(MnO2)正极的重要改性策略，其中参数的选择直接影响电化学性能。然而，掺杂元素类型、浓度以及合成条件与电化学性能之间的复杂相互关系，使得优化MnO2正极以获得优异电化学性能仍然具有挑战性。为了高效研究金属掺杂MnO2的性能，我们提出了一种基于文献数据的机器学习模型。在特征工程和模型选择后，我们发现极限梯度提升模型(XGB)在测试集上R2达到了0.921的高预测精度。在此之后，特征重要性分析进一步指导了一系列实验的设计，这些实验与密度泛函理论计算一致地验证了模型的准确性和可靠性。在从多个角度验证可行性后，我们进一步构建了基于该模型的在线性能预测平台，为后续研究人员提供了一个便捷的工具，帮助他们获得指导和启发。我们相信，这项工作为金属掺杂MnO2在能源存储领域的研究提供了新的视角和框架。

关键词: 数据驱动, 机器学习, 锌离子电池, 正极, 金属掺杂二氧化锰

Abstract:

Metal doping is a key modification strategy for MnO2 cathodes in aqueous zinc-ion batteries, with parameter selection directly governing the resulting electrochemical performance. However, the intricate interplay among dopant type and concentration, synthesis conditions, and electrochemical performance renders the optimization of MnO2 cathodes for high electrochemical properties still elusive. Traditional trial-and-error experimental screening is time-consuming and expensive, and developing a unified guideline on the design of metal-doped MnO2 remains a long-standing challenge. To efficiently study the performance of metal-doped MnO2, we proposed a machine learning (ML) model driven by data from the literature. A dataset was constructed from 36 articles covering 21 dopant elements, integrating elemental descriptors, synthesis parameters, and electrochemical testing conditions. After feature filtering and model evaluation, the extreme gradient boosting (XGB) model achieves a high predictive accuracy with an R2 of 0.921 on the test set. Beyond prediction, model interpretability using Shapley additive explanations (SHAP) analysis identifies the dominant factors affecting capacity, revealing the influence of current density, dopant ratio, and molecular weight. Feature importance analysis further guided the design of a series of experiments that validated the accuracy and reliability of the model. Experiments on Fe- and Ni-doped MnO2 cathodes confirmed the ability of metal doping to enhance specific capacity, and the model achieved a mean absolute error (MAE) below 12 mAh g−1 for all cases. Density functional theory (DFT) calculations further verified the molecular-level mechanism of metal doping by demonstrating that dopant incorporation modulates the electronic structure of MnO2 and narrows the bandgap, improving conductivity. The consistency between the ML results, experimental validation, and theoretical calculations highlights the robustness of the proposed framework. Having established the feasibility from multiple perspectives, we further deployed a performance prediction platform based on this model, providing a convenient tool for researchers to rapidly estimate the specific capacity of metal-doped MnO2 under user-defined conditions. This work demonstrates a comprehensive data-driven paradigm that integrates ML, experimental validation, and theoretical calculations. We believe that this approach provides a new strategy and framework for the rational design of high-performance MnO2 cathodes, and is broadly applicable for accelerating the discovery of other metal-doped energy storage materials.

Key words: Data driven, Machine learning, Zinc-ion battery, Cathode, Metal-doped manganese dioxide