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Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning
Author(s) -
Xu Hongyi,
Zhu Juner,
Finegan Donal P.,
Zhao Hongbo,
Lu Xuekun,
Li Wei,
Hoffman Nathaniel,
Bertei Antonio,
Shearing Paul,
Bazant Martin Z.
Publication year - 2021
Publication title -
advanced energy materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.08
H-Index - 220
eISSN - 1614-6840
pISSN - 1614-6832
DOI - 10.1002/aenm.202003908
Subject(s) - microstructure , materials science , characterization (materials science) , lithium (medication) , electrochemistry , lithium ion battery , battery (electricity) , ion , electrode , nanotechnology , biological system , composite material , thermodynamics , medicine , power (physics) , chemistry , physics , quantum mechanics , endocrinology , biology
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.