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Machine Learning-Assisted Microstructural Quantification of Multiphase Cathode Composites in All-Solid-State

Heesu Hwang1, Hyeseong Jeong2,3, Jeong-Won Cho1

  • 1Department of Materials Science and Engineering, Hongik University, Seoul, 04066, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|January 19, 2025
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Summary
This summary is machine-generated.

This study introduces machine learning for analyzing electron microscopy images of all-solid-state batteries (ASSBs). This method enables quantitative microstructural analysis to enhance battery performance and material development.

Keywords:
all‐solid‐state batterybattery performanceelectron micrographsmachine learningssemantic segmentations

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Data Science

Background:

  • Optimizing microstructure and materials is key for high-performance all-solid-state batteries (ASSBs).
  • Electron microscopy images are vital for ASSB analysis but often underutilized or qualitatively assessed.
  • Accurate quantitative analysis is needed to fully leverage microstructural data for ASSB improvement.

Purpose of the Study:

  • To explore machine learning (ML)-based quantitative analysis of electron microscopy images for ASSB microstructural characterization.
  • To apply ML combined with stereology and semantic segmentation for extracting quantitative microstructural parameters.
  • To demonstrate the utility of ML-assisted image analytics for optimizing ASSB performance.

Main Methods:

  • Utilized machine learning algorithms for quantitative analysis of electron microscopy images.
  • Integrated stereology-driven linear-intercept methods with semantic segmentation.
  • Applied ML-assisted image analytics to composite cathodes in ASSBs for microstructural characterization.

Main Results:

  • Successfully extracted quantitative microstructural parameters from electron microscopy images.
  • Demonstrated unbiased automation and deep semantic segmentation in microstructural characterization.
  • Showcased the applicability of ML-assisted techniques for ASSB material optimization.

Conclusions:

  • ML-based quantitative image analysis offers a powerful approach for ASSB research.
  • This method enhances the utilization of electron microscopy data for material development and performance evaluation.
  • The study discusses the advantages and disadvantages of ML-assisted microstructural characterization in battery science.