Density Functional Theory and Machine Learning-Based Analyses for Improved Surface Stability of a BaTiO3-Coated LiCoO2 Positive Electrode Material

Kunihiro Ishida, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Takashi Teranishi, Naoki Watanabe

Research output: Contribution to journalArticlepeer-review

Abstract

The application of oxide coating on the surfaces of active cathode materials is an effective method for improving the electrochemical durability of lithium-ion batteries because it suppresses oxygen gas release from the surface of the cathode material. This report summarizes a study conducted on verifying the suppression of oxygen release from a LiCoO2 cathode material using BaTiO3 (BT), which has recently attracted attention as a new coating material. The use of first-principles calculations and machine learning as verification methods is described. In addition to the discussion of interfacial properties based on atomic- and electronic-level considerations, comprehensive verification of the interface with several junction patterns is described. The verification results suggest that the desorption of oxygen from the surface of the active material is hindered by the oxide coating, indicating the effectiveness of BT as a coating material.

Original languageEnglish
JournalPhysica Status Solidi (B) Basic Research
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • BaTiO
  • density functional theory
  • formation energies
  • lithium-ion batteries
  • machine learning
  • oxygen vacancies
  • solid–solid interfaces

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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