Prediction of Energy Storage Performance in Polymer Composites Using High‐Throughput Stochastic Breakdown Simulation and Machine Learning-Reference-Cited by-同舟云学术

Prediction of Energy Storage Performance in Polymer Composites Using High‐Throughput Stochastic Breakdown Simulation and Machine Learning

Published:2022-04-10 Issue:17 Volume:9 Page:2105773
ISSN:2198-3844
Container-title:Advanced Science
language:en
Short-container-title:Advanced Science

Author:

Yue Dong¹²,Feng Yu²^ORCID,Liu Xiao‐Xu³,Yin Jing‐Hua¹²,Zhang Wen‐Chao²,Guo Hai⁴,Su Bo²,Lei Qing‐Quan²

Affiliation:

1. School of Materials Science and Chemical Engineering Harbin University of Science and Technology Harbin 150080 China

2. Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Harbin University of Science and Technology Harbin 150080 China

3. School of Materials Science and Engineering Shaanxi University of Science and Technology Xi'an 710021 China

4. College of Computer Science and Technology Dalian Minzu University Dalian 116650 China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202105773

Reference53 articles.

1. Grain-orientation-engineered multilayer ceramic capacitors for energy storage applications

2. Flexible Nanodielectric Materials with High Permittivity for Power Energy Storage

3. Ultrahigh β-phase content poly(vinylidene fluoride) with relaxor-like ferroelectricity for high energy density capacitors

4. Ultrahigh Breakdown Strength and Improved Energy Density of Polymer Nanocomposites with Gradient Distribution of Ceramic Nanoparticles

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