Interval support vector regression enables high-throughput machine learning predictions for dielectric constant of polymer dielectrics

Yi, Yong; Wang, L. M.; Yin, Fanghui

doi:10.1063/5.0046854

Cited by 7 publications

(7 citation statements)

References 21 publications

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Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

“…Yi et al. [ 100 ] collected a dataset containing more than 3500 experimental dielectrics from publications, with 322 atomic and block level features to describe morphological information of dielectrics. After comparing 7 modeling algorithms for model construction, PSO‐ISVR achieves the best predictive performance with the lowest MSE of 0.8987.…”

Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

“…Polymer-based dielectrics with a wide dielectric constant range perform the great potential in the applications of energy storage, high-voltage cable, and artificial muscle. Yi et al [100] collected a dataset containing more than 3500 experimental dielectrics from publications, with 322 atomic and block level features to describe morphological information of dielectrics. After comparing 7 modeling algorithms for model construction, PSO-ISVR achieves the best predictive performance with the lowest MSE of 0.8987.…”

Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

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New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Chen

et al. 2022

Advcd Theory and Sims

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Under the guidance of the material genome initiative (MGI), the use of data‐driven methods to discover new materials has become an innovation of materials science. The polymer materials have been one of the most important parts in materials science for the excellent physical and chemical properties as well as corresponding complex structures. Machine learning, as the core of data‐driven methods, has taken an important place in polymer materials design and discovery. In this review, the authors have introduced the applications of machine learning in the design and discovery of polymer materials. The development tendency of published papers about machine learning in polymer materials, the commonly used algorithms, the polymer descriptors, the workflow of machine learning in polymer materials, and recent progresses of machine learning in materials are summarized. Then, the detail of how to use machine learning to assist design and discovery of polymer materials is fully discussed combined with two cases. Finally, the opportunities and challenges on the future development prospects of machine learning in the field of polymer materials are proposed.

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Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

See 1 more Smart Citation

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Chen

et al. 2022

Advcd Theory and Sims

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“…dielectrics at various frequencies and to accelerate the agile search for polymers with desirable properties [86]. In this work, the hyper-parameters in SVM are optimized by the PSO algorithm.…”

Section: Algorithmmentioning

confidence: 99%

“…Yin et al. proposed an interval support vector regression with particle swarm optimization (PSO) to predict the dielectric constant for polymer dielectrics at various frequencies and to accelerate the agile search for polymers with desirable properties [86]. In this work, the hyper‐parameters in SVM are optimized by the PSO algorithm.…”

Section: Strategiesmentioning

confidence: 99%

Review of machine learning‐driven design of polymer‐based dielectrics

Zhu

Deng

Lei

et al. 2021

IET Nanodielectrics

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Polymer-based dielectrics are extensively applied in various electrical and electronic devices such as capacitors, power transmission cables and microchips, in which a variety of distinct performances such as the dielectric and thermal properties are desired. To fulfil these properties, the emerging machine learning (ML) technique has been used to establish a surrogate model for the structure-property linkage analysis, which provides an effective tool for the rational design of the chemical and morphological structure of polymers/nanocomposites. In this article, the authors reviewed the recent progress in the ML algorithms and their applications in the rational design of polymer-based dielectrics. The main routes for collecting training data including online libraries, experiments and high-throughput computations are first summarized. The fingerprints charactering the microstructures of polymers/nanocomposites are presented, followed by the illustration of ML models to establish a mapping between the fingerprinted input and the target properties. Further, inverse design methods such as evolution searching strategies and generative models are described, which are exploited to accelerate the discovery of new polymer-based dielectrics. Moreover, structure-property linkage analysis techniques such as Pearson correlation calculation, decision-tree-based methods and interpretable neural networks are summarized to identify the key features affecting the target properties. The future development prospects of the ML-driven design method for polymer-based dielectrics are also presented in this review. K E Y W O R D S fingerprinting, inverse design, machine learning, polymer-based dielectrics, structure-property linkage analysisThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

Deng

Jia²

2022

Fluid Phase Equilibria

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Interval support vector regression enables high-throughput machine learning predictions for dielectric constant of polymer dielectrics

Cited by 7 publications

References 21 publications

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Review of machine learning‐driven design of polymer‐based dielectrics

Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

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