Stephen McCoy, Damilola Ojedeji, Brendan Abolins, Cameron Brown, Manolis Doxastakis, Ioannis Sgouralis
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引用次数: 0
Abstract
Statistical 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.
期刊介绍:
Macromolecular Theory and Simulations is the only high-quality polymer science journal dedicated exclusively to theory and simulations, covering all aspects from macromolecular theory to advanced computer simulation techniques.