Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Abstract

Electrochemical and mechanical properties of lithium‐ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium‐ion cells. To facilitate the establishment of microstructure‐resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure‐resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium‐ion cells are presented.

Cycling Rate‐Induced Spatially‐Resolved Heterogeneities in Commercial Cylindrical Li‐Ion Batteries

“…[ 28,53,59 ] These results clearly demonstrate the value of chemical imaging and its potential to create new/validate existing electrode, cell, and multi‐scale physical models of working Li‐ion cylindrical cells which are essential in battery management systems. [ 60–64 ] Regarding the cathode, no variations were observed in the lattice parameters of the layered oxide for either charged cell; this indicates uniform delithiation.…”

Machine learning in energy storage materials

“…ML could be an effective tool to assist in analyzing the characterization results, such as resolution enhancement, real-time processing, [171] Copyright 2019, Wiley-VCH identification and segmentation, reconstruction, and so forth. [154,[182][183][184][185] An interesting application of ML is constructing an artificial intelligence atomic force microscope (AI-AFM) system. [186] As shown in Figure 10A, this system is capable of not only pattern recognition and feature identification in electrochemical systems and ferroelectric materials but can also classify via adaptive experimentation with additional probing at critical locations like domain wall and grain boundaries.…”

A review of the recent progress in battery informatics

“…The workflow of inverse design generally includes three essential steps of data generation, training ML models to predict the electrochemical performance directly from the input of microstructural parameters and applying the ML models in the inverse design of microstructures to optimize the electrochemical performance (Fig. 9e) 221 . Duquesnoy et al used the experimental data of LiNi 1/3 Mn 1/3 Co 1/3 O 2 composite electrode calendaring results to fit mathematical expression of process parameters and microstructure features 222 .…”