Citation: Yucheng Shan, Liming Xu, Peng Sun, Zhijing Zhu, Chenglong Wang, Jinliang Li, Guang Yang, Likun Pan. A data-driven approach for rapid revealing of metal doping in MnO₂ cathodes for high-performance aqueous zinc-ion batteries[J]. Acta Physico-Chimica Sinica, ;2026, 42(7): 100232. doi: 10.1016/j.actphy.2025.100232 shu

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

Yucheng Shan ¹ ,
Liming Xu ³ ,
Peng Sun ² ,
Zhijing Zhu ⁴ ,
Chenglong Wang ^1,, ,
Jinliang Li ^2,, ,
Guang Yang ¹ ,
Likun Pan ^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

Corresponding author: Chenglong Wang, Jinliang Li, Likun Pan,
Received Date: 1 October 2025
Revised Date: 2 December 2025
Accepted Date: 17 December 2025

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.
- Data driven,
- Machine learning,
- Zinc-ion battery,
- Cathode,
- Metal-doped manganese dioxide
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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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

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通讯作者: 陈斌, bchen63@163.com

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