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Related Experiment Video

Updated: Jun 28, 2026

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy
07:20

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Published on: January 20, 2023

Machine-Learning Framework for Designing Stable Interfaces in All-Solid-State Lithium-Ion Batteries.

Sehyeok Park1, Myeongcho Jang1,2, Hun-Gi Jung1,3,4

  • 1Energy Storage Research Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine-learning approach to discover stable interfacial coatings for all-solid-state lithium-ion batteries. This method predicts coating reactivity, enabling the design of more durable next-generation energy storage solutions.

Keywords:
ML‐guided screeningall‐solid‐state lithium‐ion batteriescoating materialsinterfacial stabilitysupervised learningunsupervised learning

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • All-solid-state lithium-ion batteries offer enhanced safety and energy density.
  • Interfacial instability between cathodes and solid electrolytes hinders battery longevity.
  • Current coating discovery is limited by vast chemical spaces and incomplete data.

Purpose of the Study:

  • To formulate coating discovery as a prediction-and-design problem using interfacial reaction energies.
  • To develop a machine-learning model for predicting coating reactivity.
  • To identify a design envelope for low-reactivity interfacial coatings.

Main Methods:

  • Calculated reaction energies for 809 Li-containing compounds against oxide cathodes and sulfide electrolytes.
  • Employed unsupervised clustering to group compounds by reactivity.
  • Developed an ensemble regressor using composition-derived descriptors to predict reaction energies.

Main Results:

  • Identified distinct reactivity groups and composition signatures for low-reactivity coatings.
  • Defined a design envelope characterized by polyanion tolerance and fixed-valence cations.
  • Successfully enumerated and validated new coating compositions within the predicted envelope.

Conclusions:

  • The developed workflow enables interpretable, machine-learning-guided discovery of novel interfacial coatings.
  • This approach facilitates scalable expansion beyond existing material databases.
  • The findings pave the way for more durable all-solid-state lithium-ion batteries.