Machine learning assisted synthesis of lithium-ion batteries cathode materials

Machine learning assisted synthesis of lithium-ion batteries cathode materials

Optimizing synthesis parameters is crucial in fabricating an ideal cathode material; however, the design space is too vast to be fully explored using an Edisonian approach. Here, by clustering eleven domain-expert-derived-descriptors from literature, we use an inverse design surrogate model to build...

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Bibliographic Details
Main Authors: Agar, J.C (Author), Bang, K. (Author), Baucour, A. (Author), Byon, H.R (Author), Cho, E. (Author), Cho, S. (Author), Choe, J. (Author), Hong, S. (Author), Hwang, G. (Author), Kang, H. (Author), Kim, S. (Author), Lee, H.M (Author), Lee, Y. (Author), Liow, C.H (Author), Na, M. (Author), Park, G. (Author), Shim, Y. (Author), Shin, J. (Author), Yeom, J. (Author), Yuk, J.M (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
Cathodes
Cathodes material
Clusterings
Descriptors
Design spaces
Design-to-device pipeline
Domain experts
Inverse design
Inverse designs
Inverse problems
Lithium-ion batteries
Machine learning
NCM cathode
Pipelines
Surrogate modeling
Synthesis parameters
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