Quantitative Structure‐Property Relations for Polyester Materials via Statistical Learning-Reference-Cited by-同舟云学术

Quantitative Structure‐Property Relations for Polyester Materials via Statistical Learning

Published:2024-05-20 Issue:4 Volume:33 Page:
ISSN:1022-1344
Container-title:Macromolecular Theory and Simulations
language:en
Short-container-title:Macro Theory & Simulations

Author:

McCoy Stephen¹,Ojedeji Damilola²,Abolins Brendan³,Brown Cameron³,Doxastakis Manolis²,Sgouralis Ioannis¹^ORCID

Affiliation:

1. Department of Mathematics University of Tennessee Knoxville Knoxville TN 37916 USA

2. Department of Chemical and Biomolecular Engineering University of Tennessee Knoxville Knoxville TN 37996 USA

3. Eastman Chemical Company Kingsport TN 37660 USA

Abstract

AbstractStatistical learning is employed to present a principled framework for the establishment of quantitative structure‐property relationships (QSPR). Property predictions of industrial polymers formed by multiple reagents and at varying molecular weights are focused. A theoretical description of QSPR as well as a rigorous mathematical method is developed for the assimilation of experimental data. Results show that these methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters.

Funder

Eastman Chemical Company

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mats.202400008

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4. Quantitative structure-property relationship (QSPR) framework assists in rapid mining of highly Thermostable polyimides