Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential

We investigate how Co migration from LiCoO<sub>2</sub> into the solid electrolyte LATP impacts Li-ion transport using fine-tuned machine learning interatomic potentials. Our simulations reveal that Co substitution at Ti sites induces local Li-ion trapping and disrupts long-range diffusion pathways. This study provides atomistic insights into interfacial resistance in all-solid-state batteries and highlights a predictive framework for interface design.

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Publication Details

Journal
Chemical Communications
Published
2025-01-01
DOI
https://doi.org/10.1039/d5cc03941j
Primary Topic
Advancements in Battery Materials
Type
article
Field-Weighted Citation Impact
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article

Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential

Hong‐Kang Tian, Yu-Ting Tai
Chemical Communications
Advancements in Battery Materials
article

Revealing cobalt-induced Li-ion trapping at the LATP/LCO interface with a fine-tuned machine learning interatomic potential

Hong‐Kang Tian, Yu-Ting Tai
article en

Abstract

We investigate how Co migration from LiCoO<sub>2</sub> into the solid electrolyte LATP impacts Li-ion transport using fine-tuned machine learning interatomic potentials. Our simulations reveal that Co substitution at Ti sites induces local Li-ion trapping and disrupts long-range diffusion pathways. This study provides atomistic insights into interfacial resistance in all-solid-state batteries and highlights a predictive framework for interface design.

Chemical CommunicationsVol. 61(84)
National Taiwan University of Science and Technology (TW), National Cheng Kung University (TW)
Openalex Percentile: Top 74%
Advancements in Battery Materials
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