Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Su, Zeliang; Decencière, Etienne; Nguyen, Tuan-Tu; El-Amiry, Kaoutar; De Andrade, Vincent; Franco, Alejandro A.; Demortière, Arnaud

doi:10.1038/s41524-022-00709-7

Title: Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Journal Article · 08 February 2022 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-022-00709-7 · OSTI ID:1894650

Su, Zeliang ^[1]; Decencière, Etienne ^[2]; Nguyen, Tuan-Tu ^[3]; El-Amiry, Kaoutar ^[1]; De Andrade, Vincent ^[4];

^[5];

^[6]

Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France)
MINES ParisTech – PSL Research Univ., Fontainebleau (France)
Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France); Renault Technocentre, Guyancourt (France)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France); Inst. Univ. de France, Paris (France); ALISTORE-European Research Inst. (France)
Univ. de Picardie Jules Verne (France); Réseau sur le Stockage Electrochimique de l’Energie (RS2E) (France); ALISTORE-European Research Inst. (France)

The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1894650

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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