Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images > Journals | KAIST MII LAB

Publication

Materials Imaging & Integration

Journals

Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images

Author: Jimin Oh, Jiwon Yeom, Benediktus Madika, Kwang Man Kim, Chi Hao Liow, Joshua C. Agar & Seungbum Hong

Journal: npj Computational Materials

Vol: 10 (88)

Year: 2024

Link: https://doi.org/10.1038/s41524-024-01279-6 2275회 연결

목록

이전글Balancing Ionic and Electronic Conduction at the LiFePO4 Cathode–Electrolyte Interface and Regulating Solid Electrolyte Interphase in Lithium-Ion Batteries 17.03.10
다음글Data-driven design of NASICON-type electrodes using graph-based neural networks 17.03.10