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DOE OSTI · 3020361

Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes

Xin, Huolin [Univ. of California, Irvine, CA (United States)] (ORCID:000000026521868X)
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Abstract

The objective of this project is to develop machine learning-assisted electron microscopy, together with three-dimensional, cryogenic, and in-situ imaging techniques, for resolving chemical inhomogeneity and materials dynamics in lithium battery electrodes and interfaces. The project aims to expand the spatial, temporal, and dimensional resolution of transmission electron microscopy and to enable quantitative analysis of beam-sensitive battery materials.

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25 ENERGY STORAGEbatterymachine learningtransmission electron microscopy

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Xin, Huolin [Univ. of California, Irvine, CA (United States)] (ORCID:000000026521868X). 2025-11-24. Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes. https://doi.org/10.2172/3020361

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