Authors: Ishraque Zaman Borshon | Vahid Jabbari | Todd A. Kingston | Farzad Mashayek | Reza Shahbazian-Yassar | Vitaliy Yurkiv
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Deep Learning Analysis of Solid Electrolyte Interface Microstructures in Lithium-Ion Batteries Understanding the structure of the solid-electrolyte interface (SEI) in lithium-ion batteries (LIBs) is crucial for improving battery performance and safety. This study introduces a novel deep learning (DL) framework to analyze transmission electron microscopy (TEM) images of SEI, specifically focusing on identifying different types of grains and grain boundaries. Utilizing advanced machine learning (ML) algorithms, including deep convolutional neural networks, our approach enables the accurate segmentation and classification of complex microstructural features in SEI layers based on the experimental high-resolution TEM images. The DL model was trained on a comprehensive dataset of simulated SEI TEM images, generated through density functional theory (DFT) calculations and cryogenic high-resolution TEM (cryo-HRTEM) imaging. These simulations provided a diverse range of SEI microstructures, reflecting the polycrystalline nature of real battery interfaces. The model's ability to discern between various grains and grain boundaries in the SEI was fine-tuned through this training process, ensuring high accuracy and reliability in subsequent experimental image analyses. Experimental validation was performed using cryo-HRTEM images of lithium-based batteries' SEI layers. The results demonstrated the model's proficiency in identifying and classifying different grain and grain boundary types, with significant accuracy. Particularly, the model successfully mapped the distribution of Li 2 O, LiF, and Li 2 CO 3 components within the SEI, as well as adeptly identified the crystallographic orientations of grains, such as the LiF(111)/Li 2 CO 3 (200) and Li 2 O(111)/Li 2 CO 3 (111) boundaries, unraveling the complex structure of the SEI at a nanoscopic level. Consequently, these structural features were correlated with ion diffusion pathways and potential sites for lithium dendrite formation – a detrimental phenomenon in LIBs leading to thermal runaway. This DL-based analysis provides new insights into the microscopic mechanisms governing ion transport and degradation in battery SEI layers. By offering a detailed characterization of grain and grain boundary structures, our method sets the groundwork for targeted improvements in battery design and materials selection. The study underscores the potential of integrating ML with advanced microscopy techniques to unravel the complex interplays within battery materials, propelling the field towards more resilient and efficient energy storage solutions. VY and TAK gratefully acknowledge financial support from the Department of Defense (DoD) Office of Naval Research (ONR) through the Defense Established Program to Stimulate Competitive Research (DEPSCoR). Electrolyte ; Lithium (medication) ; Microstructure ; Materials science ; Ion ; Fast ion conductor ; Chemical engineering ; Computer science ; Metallurgy ; Chemistry ; Psychology ; Engineering ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials