Modelling Structure-Property Relationship for Copolymers by Structured Representation of Repeating Units

被引：0
|
作者：
Bertinetto, Carlo [1 ]
Duce, Celia [1 ]
Micheli, Alessio [2 ]
Starita, Antonina [2 ]
Solaro, Roberto [1 ]
Tine, Maria R. [1 ]
机构：
[1] Univ Pisa, Dept Chem & Ind Chem, Via Risorgimento 35, I-56126 Pisa, Italy
[2] Univ Pisa, Dept Comp Sci, I-56127 Pisa, Italy
来源：
COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING, VOL 2: ADVANCES IN COMPUTATIONAL SCIENCE | 2009年 / 1148卷
关键词：
Recursive Neural Network; QSAR/QSPR; Poly(meth)acrylate; Copolymer; Glass Transition; GLASS-TRANSITION TEMPERATURES; COMBINATORIAL LIBRARIES; BIODEGRADABLE POLYMERS; MOLECULAR-STRUCTURES; PREDICTION; QSPR;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We report here a recent study on the prediction by recursive neural network of the glass transition temperature of (meth)acrylic copolymers, for which appropriate structured representations are proposed. It is shown that the flexibility of such description allows for simultaneously treating different classes of compounds as well as accounting for different average properties such as tacticity and molar composition.
引用
收藏
页码：33 / +
页数：2
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