Machine-learning-driven global exploration reveals atomic-scale degradation of LiNiO2 during delithiation

Chongteng Wu; Dongrui Xu; Yongjian Li; Tong Liu; Junhao Xie; Weiqiang Luo; Zhenwei Wu; Yuefeng Su; Duanyun Cao

doi:10.1039/d6ta00217j

January 1, 2026Journal of Materials Chemistry A

Machine-learning-driven global exploration reveals atomic-scale degradation of LiNiO2 during delithiation

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Key Points

This research aims to uncover the atomic-scale changes and degradation mechanisms in LiNiO2 during the delithiation process.
Employs machine learning algorithms to analyze structural changes at the atomic level.
Focuses on high-nickel content in lithium-ion battery cathodes.
Investigates the implications for energy density and safety concerns.
Finds significant structural degradation in LiNiO2 during delithiation, impacting performance.
Highlights a correlation between increased nickel content and elevated degradation rates.

Implication

Examines degradation in LiNiO2 during delithiation, suggesting insights for battery safety.

Abstract

Increasing the nickel content enhances the energy density of lithium-ion battery (LIB) cathodes, yet Ni-rich layered materials suffer from structural degradation and safety concerns. To address these, we choose LiNiO2...

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Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/6a2a510680c8f91e7f39d6b5 https://doi.org/10.1039/d6ta00217j

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Machine-learning-driven global exploration reveals atomic-scale degradation of LiNiO2 during delithiation

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine-learning-driven global exploration reveals atomic-scale degradation of LiNiO2 during delithiation

Key Points

Implication

Abstract

Discussion

Cite This Study

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