Journal article
Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images
npj computational materials, v 10(1), pp 88-9
01 Dec 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2 (NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.
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Details
- Title
- Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images
- Creators
- Jimin OhJiwon Yeom - Korea Advanced Institute of Science and TechnologyBenediktus Madika - Korea Advanced Institute of Science and TechnologyKwang Man Kim - Electronics and Telecommunications Research InstituteChi Hao Liow - Korea Advanced Institute of Science and TechnologyJoshua C Agar - Drexel UniversitySeungbum Hong - Korea Advanced Institute of Science and Technology
- Publication Details
- npj computational materials, v 10(1), pp 88-9
- Publisher
- Nature Publishing Group
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:001221448000001
- Scopus ID
- 2-s2.0-85192097029
- Other Identifier
- 991021878013704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Physical
- Materials Science, Multidisciplinary