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

• Published in: Advanced energy materials Vol. 11; no. 19
• Main Authors: Xu, Hongyi, Zhu, Juner, Finegan, Donal P., Zhao, Hongbo, Lu, Xuekun, Li, Wei, Hoffman, Nathaniel, Bertei, Antonio, Shearing, Paul, Bazant, Martin Z.
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01-05-2021; Wiley
• Subjects: computational design; electrochemical properties; ENERGY STORAGE; Image reconstruction; Lithium; Lithium-ion batteries; Machine learning; Material properties; Mechanical properties; microscopic imaging; Microstructure; Modelling; Morphology; multiphysics modeling; Prediction models
• ISSN: 1614-6832; 1614-6840
• DOI: 10.1002/aenm.202003908

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.
Heterogeneous microstructures play a critical role in determining the electrochemical and mechanical performances of Li‐ion batteries. Toward computational design of novel battery materials, the recent developments in 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 are highlighted.