Quantitative property-structural relation modeling on polymeric dielectric materials

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Authors
Wu, Ke
Issue Date
2016-05
Type
Electronic thesis
Thesis
Language
ENG
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Chemistry and chemical biology
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Abstract
Nowadays, polymeric materials have attracted more and more attention in dielectric applications. But searching for a material with desired properties is still largely based on trial and error. To facilitate the development of new polymeric materials, heuristic models built using the Quantitative Structure Property Relationships (QSPR) techniques can provide reliable ”working solutions”. In this thesis, the application of QSPR on polymeric materials is studied from two angles: descriptors and algorithms. A novel set of descriptors, called infinite chain descriptors (ICD), are developed to encode the chemical features of pure polymers. ICD is designed to eliminate the uncertainty of polymer conformations and inconsistency of molecular representation of polymers. Models for the dielectric constant, band gap, dielectric loss tangent and glass transition temperatures of organic polymers are built with high prediction accuracy. Two new algorithms, the physics-enlightened learning method (PELM) and multi-mechanism detection, are designed to deal with two typical challenges in material QSPR. PELM is a meta-algorithm that utilizes the classic physical theory as guidance to construct the candidate learning function. It shows better out-of-domain prediction accuracy compared to the classic machine learning algorithm (support vector machine). Multi-mechanism detection is built based on a cluster-weighted mixing model similar to a Gaussian mixture model. The idea is to separate the data into subsets where each subset can be modeled by a much simpler model. The case study on glass transition temperature shows that this method can provide better overall prediction accuracy even though less data is available for each subset model.
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May 2016
School of Science
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Rensselaer Polytechnic Institute, Troy, NY
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