Prediction of enthalpy of alkanes by the use of radial basis function neural networks.
A new method for the prediction of enthalpy of alkanes between C6 and C10 from molecular structures has been proposed. Thirty five calculated descriptors were selected for the description of molecular structures. The first four scores of Principle Component Analysis on the calculated descriptors were used as inputs to predict the enthalpy of alkanes. Models relating relationships between molecular structure descriptors and enthalpy of alkanes were constructed by means of radial basis function neural networks. To get the best prediction results, some strategies were also employed to optimise the learning parameters of the radial basis function neural networks. For the test set, a predictive correlation coefficient of R = 0.9913 and root mean squared error of 0.5876 were obtained.
Yao
X
X
Department of Chemistry, Lanzhou University, People's Republic of China.
Zhang
X
X
Zhang
R
R
Liu
M
M
Hu
Z
Z
Fan
B
B