Study employs image-recognition AI to determine battery composition and conditions

Researchers from KAIST, ETRI, and Drexel University have developed an AI-based image recognition technology to assess battery composition and conditions accurately. Using convolutional neural networks (CNN), the AI examines surface morphology to predict elemental composition and the charge-discharge cycles of NCM cathode materials with 99.6% accuracy. This method could revolutionize battery production by enabling automated inspections and improving the reliability and performance of battery materials.

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