Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning

Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning

Summary: Understanding the formulation and manufacturing parameters that lead to higher energy density and longevity is critical to designing energy-dense graphite electrodes for battery applications. A limited dataset that includes 27 different formulation, manufacturing protocols, and performance...

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Bibliographic Details
Main Authors: Stavros X. Drakopoulos, Azarmidokht Gholamipour-Shirazi, Paul MacDonald, Robert C. Parini, Carl D. Reynolds, David L. Burnett, Ben Pye, Kieran B. O’Regan, Guanmei Wang, Thomas M. Whitehead, Gareth J. Conduit, Alexandru Cazacu, Emma Kendrick
Format: Article
Language:English
Published: Elsevier 2021-12-01
Series:Cell Reports Physical Science
Subjects:
lithium-ion batteries
graphite
electrode manufacturing
artificial intelligence
machine learning
formulation
Online Access:http://www.sciencedirect.com/science/article/pii/S2666386421004082

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http://www.sciencedirect.com/science/article/pii/S2666386421004082

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