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Author(s): Arenas, A. | Cohen, Y. | Espinosa, G. | Giralt, F. | Yaffe, D. |
Year: 2001
Citation: Ind. Eng. Chem. Res. 2001, 40, 2757-2766.
Abstract: A modified fuzzy ARTMAP neural-network-based QSPR for predicting normal boiling points, critical temperatures, and critical pressures of organic compounds was developed. Seven or eight molecular descriptors (the sum of atomic numbers; five valence connectivity indices; and the second-order kappa shape index, without or with the dipole moment) were used to describe the topological and electronic features of a heterogeneous set of 1168 organic compounds. Optimal training and testing sets were selected with fuzzy ART. The fuzzy ARTMAP models with eight descriptors as input provided the best predictive and extrapolation capabilities compared to optimal back-propagation models and group contribution methods. The absolute mean errors of predictions for the normal boiling point (1168 compounds), the critical temperature (530 compounds), and the critical pressure (463 compounds) were 2.0 K (0.49%), 1.4 K (0.24%), and 0.02 MPa (0.52%), respectively. A composite model for simultaneously estimating the three properties yielded similar results.
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Models:
Fuzzy ARTMAP,
