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 Prediction of Molecular Properties by Recursive Neural Networks. Application to the glass transition temperature of acrylic polymers
Наименование:

Prediction of Molecular Properties by Recursive Neural Networks. Application to the glass transition temperature of acrylic polymers

автор Carlo G.B. издательство Книга по Требованию Физическая химия
Cтраниц 116        мягкая обложка       
Аннотация:This master thesis focuses on the prediction of the properties of polymers, a problem not easily treatable with traditional methods based on molecular descriptors. In subsequent studies it was successfully used to predict various properties of different classes of compounds. This methodology is based on the direct treatment of molecular structure, without using numerical descriptors, and employs recursive neural networks. The study explores different representational issues and show the accuracy and flexibility of the structure-based QSPR approach. It is a promising tool in the evaluation of existing substances, as well as in the design of new materials. In the past few years, a novel approach in cheminformatics for the Quantitative Structure-Property Relationship (QSPR) analysis of physical, chemical and biological properties of chemical compounds was developed at the University of Pisa.
Автор: Carlo G.B.
Издательство: Книга по Требованию
Год:2010
Цена:
2007 руб
Нет в наличии
Поиск: Prediction of Molecular Properties by Recursive Neural Networks. Application to the glass transition temperature of acrylic polymers
НОВЫЙ ГОД СИМВОЛ 2013 ГОДАУКРАШЕНИЯ
КАРНАВАЛ НА НОВЫЙ ГОД РАСПРОДАЖА
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Russia and the international economy

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