QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random Copolymers-Reference-Cited by-同舟云学术

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QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random Copolymers

Published:2010-09-21 Issue:8-9 Volume:29 Page:635-643
ISSN:1868-1743
Container-title:Molecular Informatics
language:en
Short-container-title:Mol. Inf.

Author:

Bertinetto Carlo Giuseppe,Duce Celia,Micheli Alessio,Solaro Roberto,Tiné Maria Rosaria

Publisher

Wiley

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,Molecular Medicine,Structural Biology

Reference21 articles.

1. Relationships between T and cohesive energy: 1. Dependence of T on the composition of copolymers

2. Predicting glass transition temperatures of linear polymers, random copolymers, and cured reactive oligomers from chemical structure

3. Prediction of Polymer Glass Transition Temperatures Using a General Quantitative Structure−Property Relationship Treatment

4. Designing Diverse and Focused Combinatorial Libraries of Synthetic Polymers

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1. Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer;Engineering with Computers;2021-01-01

2. Deep Neural Networks for Multicomponent Molecular Systems;ACS Omega;2020-08-10

3. Deep Learning for Graphs;Recent Trends in Learning From Data;2020

4. Applications in Materials Science;Applied Chemoinformatics;2018-04-20

5. Prediction of elongation at break for linear polymers;Chemometrics and Intelligent Laboratory Systems;2014-12

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