利用微调机器学习原子间电位揭示LATP/LCO界面上钴诱导的锂离子捕获

IF 4.2 2区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Chemical Communications Pub Date : 2025-09-25 DOI:10.1039/d5cc03941j

Yu-Ting Tai, Hong-Kang Tian

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引用次数: 0

摘要

我们利用微调的机器学习原子间电位研究了Co从LiCoO2迁移到固体电解质LATP如何影响锂离子的输运。我们的模拟表明，Co在Ti位点的取代诱导了局部锂离子捕获并破坏了远程扩散途径。这项研究为全固态电池的界面电阻提供了原子的见解，并强调了界面设计的预测框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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 LiCoO₂ 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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来源期刊

Chemical Communications 化学-化学综合

CiteScore

8.60

自引率

4.10%

发文量

2705

审稿时长

1.4 months

期刊介绍： ChemComm (Chemical Communications) is renowned as the fastest publisher of articles providing information on new avenues of research, drawn from all the world''s major areas of chemical research.