A temperature-constrained cascade correlation network （TCCCN）, a back-propagation neural network （BP）, and multiple linear regression（MLR） models were applied to quantitative structure-activity relationship（QSA...A temperature-constrained cascade correlation network （TCCCN）, a back-propagation neural network （BP）, and multiple linear regression（MLR） models were applied to quantitative structure-activity relationship（QSAR） modeling, on the basis of a set of 35 nitrobenzene derivatives and their acute toxicities. These structural quantum-chemical descriptors were obtained from the density functional theory（DFT）. Stepwise multiple regression analysis was performed and the model was obtained. The value of the calibration correlation coefficient R is 0. 925, and the value of cross-validation correlation coefficient R is 0. 87. The standard error S = 0. 308 and the cross-validated （ leave-one- out） standard error Scv =0. 381. Principal component analysis（PCA） was carried out for parameter selection. RMS errors for training set via TCCCN and BP are 0. 067 and 0. 095, respectively, and RMS errors for testing set via TCCCN and BP are 0. 090 and 0. 111, respectively. The results show that TCCCN performs better than BP and MLR.展开更多
X-ray diffraction patterns of graphite oxide(GO)are theoretically simulated as a function of the displacements of carbon atoms using the Debye–Waller factor in terms of the Warren–Bodenstein equation.The results dem...X-ray diffraction patterns of graphite oxide(GO)are theoretically simulated as a function of the displacements of carbon atoms using the Debye–Waller factor in terms of the Warren–Bodenstein equation.The results demon-strate that GO has the turbostratically stacked structure.The high order(001)peaks gradually disappear with the increase in atomic thermal vibrations along𝑑c-axis while the(ℎk0)ones weaken for the vibrations along a-axis.When the displacement deviation𝑣ua𝑏=0.015nm and𝑣𝑑uc=0.100nm the computed result is consistent with the experimental measurements.展开更多
基金Supported by the Science and Technology Program, Beijing Municipal Education Commission(No. KM200310028105)
文摘A temperature-constrained cascade correlation network （TCCCN）, a back-propagation neural network （BP）, and multiple linear regression（MLR） models were applied to quantitative structure-activity relationship（QSAR） modeling, on the basis of a set of 35 nitrobenzene derivatives and their acute toxicities. These structural quantum-chemical descriptors were obtained from the density functional theory（DFT）. Stepwise multiple regression analysis was performed and the model was obtained. The value of the calibration correlation coefficient R is 0. 925, and the value of cross-validation correlation coefficient R is 0. 87. The standard error S = 0. 308 and the cross-validated （ leave-one- out） standard error Scv =0. 381. Principal component analysis（PCA） was carried out for parameter selection. RMS errors for training set via TCCCN and BP are 0. 067 and 0. 095, respectively, and RMS errors for testing set via TCCCN and BP are 0. 090 and 0. 111, respectively. The results show that TCCCN performs better than BP and MLR.
基金Supported by the Innovation Foundation of the Ministry of Science and Technology of China under Grant No 10C26224302621.
文摘X-ray diffraction patterns of graphite oxide(GO)are theoretically simulated as a function of the displacements of carbon atoms using the Debye–Waller factor in terms of the Warren–Bodenstein equation.The results demon-strate that GO has the turbostratically stacked structure.The high order(001)peaks gradually disappear with the increase in atomic thermal vibrations along𝑑c-axis while the(ℎk0)ones weaken for the vibrations along a-axis.When the displacement deviation𝑣ua𝑏=0.015nm and𝑣𝑑uc=0.100nm the computed result is consistent with the experimental measurements.
