AI for Lithium-ion battery research under data-scarce scenarios
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PUBLIC SUMMARY
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Root causes and consequences of data scarcity in LIB research are critically examined.
Materials-level and device-level solutions for mitigating data scarcity are systematically presented.
Future avenues for further reducing the impact of data scarcity across the field are proposed.
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ABSTRACT
The global challenge on energy crises has promoted the extensive research on energy storage, which positions the lithium-ion batteries (LIBs) as a core technology due to the exceptional performance. While conventional approaches for LIB research are limited by long development cycles, high resource demands, and heavy reliance on the expertise of researchers, data-driven methods powered by ML exhibit remarkable potentials on enabling the efficient and accurate predictions of key battery performance metrics, significantly reducing the time and economic costs on the R&D of batteries. Despite these advances, data scarcity remains a major obstacle to the widespread applications of these techniques due to the high costs and prolonged cycles associated with battery experiments. To investigate appropriate approaches for addressing these challenges, a comprehensive review on the data-driven methods for lithium battery research is provided, offering insights into mitigating the impact of data scarcity. To reach this goal, this review systematically examines the implementations of data-driven methods associated with the applications in the battery domain. Detailed studies have been conducted to investigate the root causes of data scarcity at various levels ranging from materials to devices and effective strategies to accommodate these issues. Finally, the review discusss future directions, emphasizing the need for collaborative data-sharing frameworks and adaptive ML models that balance domain expertise with computational innovation. By addressing data scarcity through these strategies, this work aims to accelerate the development of next-generation LIB while retaining the core insights of traditional methodologies.-
Keywords:
- Machine learning /
- lithium-ion battery /
- Data scarcity /
- Material development /
- Device optimization
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Zhang J., Zhai X., Zhang Q., et al. (2026). AI for Lithium-ion battery research under data-scarce scenarios. The Innovation Materials 4:100209. https://doi.org/10.59717/j.xinn-mater.2026.100209
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Zhang J., Zhai X., Zhang Q., et al. (2026). AI for Lithium-ion battery research under data-scarce scenarios. The Innovation Materials 4:100209. https://doi.org/10.59717/j.xinn-mater.2026.100209
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Content
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Figure 1.
Recent advances of ML assisted methods for LIB research.26-39
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Figure 2.
Machine learning methods to tackle data-scarcity issue.
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Figure 4.
ML assisted MD,DFT and FEM
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Figure 5.
ML image segmentation and data augmentations in material characterization
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Figure 6.
ML involved production workflow
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Figure 7.
ML guided efficient data utilization in life management
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Figure 8.
SOX estimation based on ML methodologies
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Figure 9.
ML assisted predictive and adaptive battery management
