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Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds
Juan A Lazzús
Author NameAffiliationE-mail
Juan A Lazzús Departamento de Física, Universidad de La Serena, Casilla 554, La Serena, Chile jalazzus@gmail.com 
Abstract:
The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship (QSPR) parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab.Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.
Key words:  Melting point, Quantitative structure-property relationship, Artficial neural network, Quantum chemistry
FundProject:
Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds
Juan A Lazzús *
摘要:
采用反向传播神经网络与定量结构-性质关系中量子化学参数相结合的方法测定有机化合物的熔点.11个反映分子间作用力和分子对称性的描述符作为输入变量,通过分子模型和PM3半经验分子轨道理论计算量子化学参数.用260个化合物训练由MatLab方法建立的神经网络,预测了73个化合物的熔点,并与文献中的实验数据进行比较,结果表明这种人工神经网络与定量结构-性质关系结合的方法可以预测有机化合物的熔点,平均绝对偏差5%.
关键词:  熔点,定量结构-性质关系,人工神经网络,量子化学
DOI:10.1088/1674-0068/22/01/19-26
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