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DOI of the published article https://doi.org/10.1002/mats.202400008
Preprint / Version 1

Quantitative structure-property relations for polyester materials via statistical learning

##article.authors##

  • Stephen McCoy University of Tennessee Math Department
  • Damilola Ojedeji University of Tennessee Knoxville, Department of Chemical and Biomolecular Engineering
  • Brendan Abolins Eastman Chemical Company
  • Cameron Brown Eastman Chemical Company
  • Manolis Doxastakis University of Tennessee Knoxville, Department of Chemical and Biomolecular Engineering
  • Ioannis Sgouralis University of Tennessee Knoxville, Department of Mathematics

DOI:

https://doi.org/10.31224/3565

Keywords:

qspr, polymer, glass transition temperature, Viscosity, bayesian machine learning, Statistical Learning

Abstract

We employ statistical learning to present a principled framework for the establishment of quantitative structure-property relationships (QSPR). We focus on property predictions of industrial polymers formed by multiple reagents and at varying molecular weights. We develop a theoretical description of QSPR as well as a rigorous mathematical method for the assimilation of experimental data. Results show that our methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters.

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Posted

2024-02-26

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Copyright (c) 2024 Stephen McCoy, Damilola Ojedeji, Brendan Abolins, Cameron Brown, Manolis Doxastakis, Ioannis Sgouralis

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This work is licensed under a Creative Commons Attribution 4.0 International License.