Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics

Machine learning approaches for permittivity prediction and rational design of microwave dielectric ceramics

Low permittivity microwave dielectric ceramics (MWDCs) are attracting great interest because of their promising applications in the new era of 5G and IoT. Although theoretical rules and computational methods are of practical use for permittivity prediction, unsatisfactory predictability and universa...

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Bibliographic Details
Main Authors: Jincheng Qin, Zhifu Liu, Mingsheng Ma, Yongxiang Li
Format: Article
Language:English
Published: Elsevier 2021-11-01
Series:Journal of Materiomics
Subjects:
Microwave dielectric ceramics
Low permittivity ceramics
Permittivity prediction
Machine learning
Quantitative structure-property relationship
Online Access:http://www.sciencedirect.com/science/article/pii/S2352847821000332

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http://www.sciencedirect.com/science/article/pii/S2352847821000332

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