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Related Experiment Video

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$In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries$

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Deep-Learning Aided Atomic-Scale Phase Segmentation toward Diagnosing Complex Oxide Cathodes for Lithium-Ion

Dong Zhu^1,2,3, Chunyang Wang¹, Peichao Zou¹

¹Department of Physics and Astronomy, University of California Irvine, Irvine, California 92697, United States.

|August 29, 2023

Summary

A new deep learning tool enables automated atom-by-atom phase segmentation in complex battery materials. This breakthrough facilitates understanding phase transformations and developing advanced materials.

Keywords:

cathodes deep learning lithium-ion batteries phase segmentation phase transformation scanning transmission electron microscopy

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Area of Science:

Materials Science
Artificial Intelligence
Chemistry

Background:

Phase transformations are crucial for material properties but difficult to analyze in multiphase systems.
Conventional methods lack accuracy and efficiency for complex phase segmentation.

Purpose of the Study:

To develop a deep learning tool for automated, atom-by-atom phase segmentation.
To analyze intertwined phase domains in lithium-ion battery cathode materials.

Main Methods:

Coupling a hard-attention-enhanced U-Net network with geometry simulation.
Utilizing atomic-resolution transmission electron microscopy (AR-TEM).

Main Results:

Successful automated segmentation of multiphase domains in cathode materials.
Demonstrated superior performance compared to traditional phase segmentation methods.
Quantitatively revealed correlations between multiple phases during battery operation.

Conclusions:

Deep learning offers a powerful approach for in-depth understanding of phase transformations in complex materials.
The developed tool advances materials development for lithium-ion batteries.