We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning.
- Authors
Hongyi Xu; Juner Zhu; Finegan, Donal P.; Hongbo Zhao; Xuekun Lu; Wei Li; Hoffman, Nathaniel; Bertei, Antonio; Shearing, Paul; Bazant, Martin Z.
- 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.
- Subjects
MACHINE learning; LITHIUM-ion batteries; PREDICTION models; MICROSTRUCTURE; MECHANICAL properties of condensed matter; ELECTRODES; ALUMINUM-lithium alloys
- Publication
Advanced Energy Materials, 2021, Vol 11, Issue 19, p1
- ISSN
1614-6832
- Publication type
Article
- DOI
10.1002/aenm.202003908