title: Predicting the standard enthalpy (deltaH0f) and entropy (S0) of alkanes by artificial neural networks.
abstract: Artificial Neural Networks (ANNs) with Extended Delta-Bar-Delta (EDBD) back propagation learning algorithm have been developed to predict the standard enthalpy and entropy of 87 acyclic alkanes. Molecular weight, boiling point and density of the compounds were used as input parameters. The network's architecture and parameters were optimized to give maximum performances. The best network was a 3-6-2 ANN, and the optimum learning epoch was about 1320. The results show that the maximum relative errors of enthalpy and entropy are less than 3%. They reveal that the performances of ANNs for predicting the enthalpy and entropy of alkanes are satisfying.
authors.affiliation: Department of Chemistry, Lanzhou University, PR China.
authors.forename: A
authors.initials: A
authors.lastname: Yan
authors.forename: X
authors.initials: X
authors.lastname: Chen
authors.forename: R
authors.initials: R
authors.lastname: Zhang
authors.forename: M
authors.initials: M
authors.lastname: Liu
authors.forename: Z
authors.initials: Z
authors.lastname: Hu
authors.forename: B T
authors.initials: BT
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D000473:Alkanes / Q000737:chemistry*; D019277:Entropy; D005544:Forecasting; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000473:Alkanes