Designing polymer nanocomposites with high energy density using machine learning

Abstract Addressing microstructure-property relations of polymer nanocomposites is vital for designing advanced dielectrics for electrostatic energy storage. Here, we develop an integrated phase-field model to simulate the dielectric response, charge transport, and breakdown process of polymer nanoc...

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Autores principales:	Zhong-Hui Shen, Zhi-Wei Bao, Xiao-Xing Cheng, Bao-Wen Li, Han-Xing Liu, Yang Shen, Long-Qing Chen, Xiao-Guang Li, Ce-Wen Nan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/7b2534f9f7574300ada0f04efd74212a
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spelling	oai:doaj.org-article:7b2534f9f7574300ada0f04efd74212a2021-12-02T16:14:16ZDesigning polymer nanocomposites with high energy density using machine learning10.1038/s41524-021-00578-62057-3960https://doaj.org/article/7b2534f9f7574300ada0f04efd74212a2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00578-6https://doaj.org/toc/2057-3960Abstract Addressing microstructure-property relations of polymer nanocomposites is vital for designing advanced dielectrics for electrostatic energy storage. Here, we develop an integrated phase-field model to simulate the dielectric response, charge transport, and breakdown process of polymer nanocomposites. Subsequently, based on 6615 high-throughput calculation results, a machine learning strategy is schemed to evaluate the capability of energy storage. We find that parallel perovskite nanosheets prefer to block and then drive charges to migrate along with the interfaces in x-y plane, which could significantly improve the breakdown strength of polymer nanocomposites. To verify our predictions, we fabricate a polymer nanocomposite P(VDF-HFP)/Ca2Nb3O10, whose highest discharged energy density almost doubles to 35.9 J cm−3 compared with the pristine polymer, mainly benefit from the improved breakdown strength of 853 MV m−1. This work opens a horizon to exploit the great potential of 2D perovskite nanosheets for a wide range of applications of flexible dielectrics with the requirement of high voltage endurance.Zhong-Hui ShenZhi-Wei BaoXiao-Xing ChengBao-Wen LiHan-Xing LiuYang ShenLong-Qing ChenXiao-Guang LiCe-Wen NanNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Zhong-Hui Shen Zhi-Wei Bao Xiao-Xing Cheng Bao-Wen Li Han-Xing Liu Yang Shen Long-Qing Chen Xiao-Guang Li Ce-Wen Nan Designing polymer nanocomposites with high energy density using machine learning
description	Abstract Addressing microstructure-property relations of polymer nanocomposites is vital for designing advanced dielectrics for electrostatic energy storage. Here, we develop an integrated phase-field model to simulate the dielectric response, charge transport, and breakdown process of polymer nanocomposites. Subsequently, based on 6615 high-throughput calculation results, a machine learning strategy is schemed to evaluate the capability of energy storage. We find that parallel perovskite nanosheets prefer to block and then drive charges to migrate along with the interfaces in x-y plane, which could significantly improve the breakdown strength of polymer nanocomposites. To verify our predictions, we fabricate a polymer nanocomposite P(VDF-HFP)/Ca2Nb3O10, whose highest discharged energy density almost doubles to 35.9 J cm−3 compared with the pristine polymer, mainly benefit from the improved breakdown strength of 853 MV m−1. This work opens a horizon to exploit the great potential of 2D perovskite nanosheets for a wide range of applications of flexible dielectrics with the requirement of high voltage endurance.
format	article
author	Zhong-Hui Shen Zhi-Wei Bao Xiao-Xing Cheng Bao-Wen Li Han-Xing Liu Yang Shen Long-Qing Chen Xiao-Guang Li Ce-Wen Nan
author_facet	Zhong-Hui Shen Zhi-Wei Bao Xiao-Xing Cheng Bao-Wen Li Han-Xing Liu Yang Shen Long-Qing Chen Xiao-Guang Li Ce-Wen Nan
author_sort	Zhong-Hui Shen
title	Designing polymer nanocomposites with high energy density using machine learning
title_short	Designing polymer nanocomposites with high energy density using machine learning
title_full	Designing polymer nanocomposites with high energy density using machine learning
title_fullStr	Designing polymer nanocomposites with high energy density using machine learning
title_full_unstemmed	Designing polymer nanocomposites with high energy density using machine learning
title_sort	designing polymer nanocomposites with high energy density using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7b2534f9f7574300ada0f04efd74212a
work_keys_str_mv	AT zhonghuishen designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT zhiweibao designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT xiaoxingcheng designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT baowenli designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT hanxingliu designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT yangshen designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT longqingchen designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT xiaoguangli designingpolymernanocompositeswithhighenergydensityusingmachinelearning AT cewennan designingpolymernanocompositeswithhighenergydensityusingmachinelearning
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Designing polymer nanocomposites with high energy density using machine learning

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