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Squared Correlation Coefficient (squared + correlation_coefficient)
Selected AbstractsH-bond donor strength;JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 9 2009Abraham parameter; A quantum chemical model is introduced to predict the H-bond donor strength of monofunctional organic compounds from their ground-state electronic properties. The model covers OH, NH, and CH as H-bond donor sites and was calibrated with experimental values for the Abraham H-bond donor strength parameter A using the ab initio and density functional theory levels HF/6-31G** and B3LYP/6-31G**. Starting with the Morokuma analysis of hydrogen bonding, the electrostatic (ES), polarizability (PL), and charge transfer (CT) components were quantified employing local molecular parameters. With hydrogen net atomic charges calculated from both natural population analysis and the ES potential scheme, the ES term turned out to provide only marginal contributions to the Abraham parameter A, except for weak hydrogen bonds associated with acidic CH sites. Accordingly, A is governed by PL and CT contributions. The PL component was characterized through a new measure of the local molecular hardness at hydrogen, ,(H), which in turn was quantified through empirically defined site-specific effective donor and acceptor energies, EEocc and EEvac. The latter parameter was also used to address the CT contribution to A. With an initial training set of 77 compounds, HF/6-31G** yielded a squared correlation coefficient, r2, of 0.91. Essentially identical statistics were achieved for a separate test set of 429 compounds and for the recalibrated model when using all 506 compounds. B3LYP/6-31G** yielded slightly inferior statistics. The discussion includes subset statistics for compounds containing OH, NH, and active CH sites and a nonlinear model extension with slightly improved statistics (r2 = 0.92). © 2008 Wiley Periodicals, Inc. J Comput Chem 2009 [source] Accurate prediction of thermodynamic properties of alkyl peroxides by combining density functional theory calculation with least-square calibrationJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 7 2009Cun-Xi Liu Abstract Owing to the significance in kinetic modeling of the oxidation and combustion mechanisms of hydrocarbons, a fast and relatively accurate method was developed for the prediction of ,fH of alkyl peroxides. By this method, a raw ,fH value was calculated from the optimized geometry and vibration frequencies at B3LYP/6-31G(d,p) level and then an accurate ,fH value was obtained by a least-square procedure. The least-square procedure is a six-parameter linear equation and is validated by a leave-one out technique, giving a cross-validation squared correlation coefficient q2 of 0.97 and a squared correlation coefficient of 0.98 for the final model. Calculated results demonstrated that the least-square calibration leads to a remarkable reduction of error and to the accurate ,fH values within the chemical accuracy of 8 kJ mol,1 except (CH3)2CHCH2CH2CH2OOH which has an error of 8.69 kJ mol,1. Comparison of the results by CBS-Q, CBS-QB3, G2, and G3 revealed that B3LYP/6-31G(d,p) in combination with a least-square calibration is reliable in the accurate prediction of the standard enthalpies of formation for alkyl peroxides. Standard entropies at 298 K and heat capacities in the temperature range of 300,1500 K for alkyl peroxides were also calculated using the rigid rotor-harmonic oscillator approximation. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009 [source] Accurate Prediction of , (Lower Critical Solution Temperature) in Polymer Solutions Based on 3D Descriptors and Artificial Neural NetworksMACROMOLECULAR THEORY AND SIMULATIONS, Issue 2-3 2008Jie Xu Abstract Quantitative structure-property relationships were studied between descriptors representing the three-dimensional structures of molecules and , (LCST, lower critical solution temperature) in polymer solutions with a database of 169 data containing 12 polymers and 67 solvents. Feed-forward artificial neural networks (ANNs) combined with stepwise multilinear regression analysis (MLRA) were used to develop the models. With ANNs, the squared correlation coefficient (R2) for , (LCST) of the training set of 112 systems is 0.9625, the standard error of estimation (SEE) is 13.43 K, and the mean relative error (MRE) is 1.99%; in prediction of , (LCST) using the test set of 57 systems, the MRE is 2.26%. With MLRA, the MREs for the training and test sets are 4.02% (R2,=,0.8739, SEE,=,25.88 K) and 5.05%, respectively. [source] QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random CopolymersMOLECULAR INFORMATICS, Issue 8-9 2010Carlo Giuseppe Bertinetto Abstract The glass transition temperature (Tg) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing predefined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275,copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9,K, a standard error of prediction (S) of 6.1,K and a squared correlation coefficient (R2) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting. [source] | ||||||||||