Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2087-2092 |
| Number of pages | 6 |
| Journal | Journal of the Chemical Society. Perkin Transactions 2 |
| Issue number | 12 |
| Publication status | Published - 2002 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Chemistry(all)
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Prediction of autoignition temperatures (AITs) for hydrocarbons and compounds containing heteroatoms by the quantitative structure-property relationship. / Kim, Yeong Suk; Lee, Sung Kwang; Kim, Jae Hyun; Kim, Jung Sup; No, Kyoung Tai.
In: Journal of the Chemical Society. Perkin Transactions 2, No. 12, 01.12.2002, p. 2087-2092.Research output: Contribution to journal › Article
TY - JOUR
T1 - Prediction of autoignition temperatures (AITs) for hydrocarbons and compounds containing heteroatoms by the quantitative structure-property relationship
AU - Kim, Yeong Suk
AU - Lee, Sung Kwang
AU - Kim, Jae Hyun
AU - Kim, Jung Sup
AU - No, Kyoung Tai
PY - 2002/12/1
Y1 - 2002/12/1
N2 - 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.
AB - 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.
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M3 - Article
AN - SCOPUS:0036934010
SP - 2087
EP - 2092
JO - Journal of the Chemical Society, Perkin Transactions 2
JF - Journal of the Chemical Society, Perkin Transactions 2
SN - 1470-1820
IS - 12
ER -