Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks
Quantitative microstructural characterization of Li-ion battery electrode materials using electron backscatter diffraction (EBSD) has been proven as a critical method for optimizing cell performance. However, the inherently slow nature of EBSD can hinder the throughput of analyses needed for statistical representation of a material microstructure being developed. This work demonstrates a machine learning super-resolution framework using a generative adversarial network (SRGAN) to significantly increase EBSD throughput. The SRGAN model was trained on EBSD data of LiNixMnyCozO2 (NMC) cathode par
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- PossiblePossibly related (embedding) · 47%LLNL Combines HPC and Machine Learning to Accelerate Battery Design - HPCwire →
- PossiblePossibly related (embedding) · 46%Machine learning maps the atomic complexity inside batteries - Nanowerk →
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Enhancing EBSD throughput of battery electrode materials usi” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Enhancing EBSD throughput of battery electrode materials usi” ≈ “steven2358/awesome-generative-ai””
- LinkedLinked via arxiv author · 85%John Mangum →
“Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks”
- LinkedLinked via arxiv author · 85%Andrew Glaws →
“Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks”
- LinkedLinked via arxiv author · 85%Francois Usseglio-Viretta →
“Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks”
- LinkedLinked via arxiv author · 85%Steven Spurgeon →
“Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks”