中文版 | English
Title

Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets

Author
Corresponding AuthorNO,Kyoung Tai
Publication Years
2022-08-01
DOI
Source Title
ISSN
2405-8440
EISSN
2405-8440
Volume8Issue:8
Abstract
Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles information including separation information, chemical equilibrium, chemical reactivity analysis, and solubility modeling. Since, the available ε-prediction models are fairly primitive and frequently suffer from serious failures especially when deals with strong polar compounds. Therefore, we have developed a novel data-driven system to improve the efficiency and wide-range applicability of ε using in material sciences. This innovative scheme adopts the correlation distance and genetic algorithm to discriminate features’ combination and avoid overfitting. Herein, the prediction output of the single ML model as a coding to estimate the target value by simulating the layer-by-layer extraction in deep learning, and enabling instant search for the optimal combination of features is recruited. Our model established an improved correlation value of 0.956 with target as compared to the previously available best traditional ML result of 0.877. Our framework established a profound improvement, especially for material systems possessing ε value >50. In terms of interpretability, we have derived a conceptual computational equation from a minimum generating tree. Our innovative data-driven system is preferentially superior over other methods due to its application for the prediction of dielectric constants as well as for the prediction of overall micro and macro-properties of any multi-components complex.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
WOS Research Area
Science & Technology - Other Topics
WOS Subject
Multidisciplinary Sciences
WOS Accession No
WOS:000866222700002
Publisher
Scopus EID
2-s2.0-85135915612
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/382619
DepartmentSchool of Life Sciences
生命科学学院_生物系
Affiliation
1.College of Integrative Biotechnology and Translational Medicine,Yonsei University,Incheon,(21983),South Korea
2.Department of Natural and Basic Sciences,University of Turbat,Turbat,Kech, Balochistan (92600),Pakistan
3.Department of Biotechnology,College of Life Science and Biotechnology,Yonsei University,Seoul,(03722),South Korea
4.Department of Biology,School of Life Sciences,Southern University of Science and Technology,Shenzhen,1088 Xueyuan Avenue, (518055), Guangdong,China
Recommended Citation
GB/T 7714
Mao,Jiashun,Zeb,Amir,Kim,Min Sung,et al. Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets[J]. Heliyon,2022,8(8).
APA
Mao,Jiashun.,Zeb,Amir.,Kim,Min Sung.,Jeon,Hyeon Nae.,Wang,Jianmin.,...&NO,Kyoung Tai.(2022).Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets.Heliyon,8(8).
MLA
Mao,Jiashun,et al."Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets".Heliyon 8.8(2022).
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