ArticleNature communications2021
Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 125 citations in OpenAlex.
- Physics-Guided Multi-Channel Restoration for Efficient and Accurate Atomic Force Microscopy Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-State Battery Cathodes.ACS energy letters · 2026Article
- A quantitative imaging framework for lithium morphology: Linking deposition uniformity to cycle stability in lithium metal batteries.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- X‑ray Micro-Computed Tomography for Structural Analysis of All-Solid-State Battery at Pouch Cell Level.ACS energy letters · 2025Review
- RecyBat24: a dataset for detecting lithium-ion batteries in electronic waste disposal.Scientific data · 2025Article
- Article
- Three-dimensional, multimodal synchrotron data for machine learning applications.Scientific data · 2025Article
- A comprehensive and quantitative SEM-EDS analytical process applied to lithium-ion battery electrodes.Scientific reports · 2025Article
- Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors.Science and technology of advanced materials · 2025Review
- Fracture Dynamics in Silicon Anode Solid-State Batteries.ACS energy letters · 2024Article
- Article
- Lithium Metal Battery Quality Control via Transformer-CNN Segmentation.Journal of imaging · 2023Article
- Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.
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Registered trials
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