Jung Sup Kim

Prediction of autoignition temperatures (AITs) for hydrocarbons and compounds containing heteroatoms by the quantitative structure–property relationship

Regression models that are useful for the explanation and prediction of autoignition temperatures of diverse compounds were provided by a quantitative structure–property relationships study (QSPR). Genetic functional approximation was used to find the best multiple linear regression within 72 molecular descriptors. After validation by correlation of the prediction set, nine descriptor models were evaluated in the best model. The nine descriptors were Ial, Ike, radius of gyration, 1χv, SC-2, the Balaban index JX, density, Kappa-3-AM and Jurs-FNSA-2, and information of structure features and their interactions was provide. The result of the best regression model showed that the square of the correlation coefficient (R2) for the autoignition temperature of the 157-member training set was 0.920, and the root mean square error (RMSE) was 25.876. The R2 of AIT for a 43-member prediction set was 0.910, and the RMSE was 28.968.

Prediction of glass transition temperature (Tg) of some compounds in organic electroluminescent devices with their molecular properties

We have studied the quantitative structure-property relationship between descriptors representing the molecular structure and glass transition temperature (T(g)) for 103 molecules including organic electroluminescent (EL) devices materials. Eighty-six descriptors were introduced and among them seven descriptors (one topological descriptor, one thermodynamic descriptor, one spatial descriptor, one structural descriptor, and three electrostatic descriptors) were selected by Genetic Algorithm (GA). The 81 molecules chosen randomly among 103 compounds were used as a training set, and the remaining 22 molecules were used as a prediction set. The quantitative relationship between these seven descriptors and T(g) was tested by multiple linear regression (MLR) and artificial neural network (ANN). ANN analysis showed no significant advantage over MLR for this study. As the results of the MLR, the square of the correlation coefficient (R(2)) for the T(g) of the 81 training set was 0.989, and the average error was 8.8 K. In prediction for T(g) using the 22 prediction compounds set with MLR, R(2) was 0.976, and the average error was 13.9 K.

Intraframework potential energy function of zeolites

Potential energy functions suitable for the (Cs1−xNaxT2O4)-A type zeolite family were obtained from the known crystal structure of Cs7Na5-A zeolite. Using these potential functions, several other crystal structures in these family were obtained by molecular mechanical calculations. The activation energy α- to α-cage transmission of the H2 molecule and the void volume of Cs3Na9-A zeolite were calculated.

Encapsulation of H2 Molecules in Cs3Na9-A Zeolite: A Theoretical Approach

A theoretical method to study the encapsulation of H2 molecules in the cavities of Cs3Na9-A zeolite has been proposed. To study the properties of encapsulated H2 molecules, a Fermi-Dirac like statistics has been introduced. The average binding energy per H2 is obtained as a function of the number of molecules and temperature. The average activation energy is also calculated from the minimum energy path for the α- to α-cage transmission and the average binding energy. The fraction with higher energy than its activation energy has been calculated and revealed that the activation energy for the en- and decapsulation of H2 molecules depends not only on the temperature but also on the number of the encapsulated molecules.