Machine Learning Helps Probe Sodium Ion Motion Behavior in Carbon-Based Anodes

Zihao Yang; Zhendong Liu; Quanbing Liu

doi:10.7536/PC20250613

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Prog Chem ›› 2025, Vol. 37 ›› Issue (12) : 1836-1845. DOI: 10.7536/PC20250613

Review

Machine Learning Helps Probe Sodium Ion Motion Behavior in Carbon-Based Anodes

Zihao Yang ¹^,² ,
Zhendong Liu ³ ,
Quanbing Liu ¹^,²^,^*

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Abstract

The complexity of sodium-storage mechanisms has become a key bottleneck limiting the deployment of high-performance carbon-based anodes in commercial sodium-ion batteries. In hard-carbon anodes， Na-storage involves multiscale， coupled processes that are challenging to characterize. Machine learning （ML） can bridge the experiment-characterization-simulation divide， rapidly uncover nonlinear multivariate relationships and key structure-property descriptors， complement theoretical calculations by mitigating limitations in time/length scales and data scarcity， and enable predictions of capacity plateaus， diffusion kinetics， and cycling stability. Building on a critical synthesis of Na-storage mechanisms in hard carbon， this review distills core ML strategies and representative applications to support interpretable， data-driven design of high-capacity， long-life carbon anodes， highlighting ML-centered approaches for probing alkali-ion behavior. The aim is to provide theoretical guidance and practical design rules for the future design and optimization of carbon-based anode materials.

Contents

1 Introduction

2 The principal challenges facing carbon-based anodes

2.1 Bonding behaviour of alkali metal atoms in various carbon material systems

2.2 Sodium storage behaviour in hard carbon

3 Machine learning in investigating ion transport behaviour in carbon-based anodes

3.1 Common machine learning algorithms

3.2 Data-driven machine learning approaches

3.3 Machine learning reveals intercalation behaviour in carbon materials

4 Conclusion and outlook

Key words

sodium storage mechanism / hard carbon / machine learning / material design

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Zihao Yang , Zhendong Liu , Quanbing Liu. Machine Learning Helps Probe Sodium Ion Motion Behavior in Carbon-Based Anodes[J]. Progress in Chemistry. 2025, 37(12): 1836-1845 https://doi.org/10.7536/PC20250613

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