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Prediction of the glass transition temperature of polymers using neural network and multiple linear regression

Abstract:

A nonlinear model and a linear model have been developed to correlate glass transition temperature (Tg) and repeating units of polymers using a neural network and multiple linear regression analysis respectively. A set of descriptors, chosen based on previous studies of the relations between T g and polymer structure, was used to describe the structure of repeating units, individual bond energies and intermolecular forces, especially hydrogen bonding, which is the strongest intermolecular for...

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Authors


Sztandera, L More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Chemistry, Physical and Theoretical Chem
Journal:
DWI Reports
Issue:
130
Publication date:
2006
ISSN:
0942-301X
URN:
uuid:4374109e-2737-4f52-ae39-e8d6259702b7
Source identifiers:
125421
Local pid:
pubs:125421
Language:
English

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