Prediction of the polarity parameter π for the radical derived from monomer

e-Polymers ◽  
2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Xinliang Yu ◽  
Wenhao Yu ◽  
Bing Yi ◽  
Xueye Wang
Keyword(s):  
Neural Network ◽  
Quantum Chemical ◽  
Density Functional ◽  
Ann Model ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
The Neural Network ◽  
Polarity Parameter ◽  
Chemical Descriptors ◽  
Artificial Neural Network Ann

AbstractAn artificial neural network (ANN) model was successfully developed for the modelling and prediction of the polarity parameter π used in the revised patterns scheme for the prediction of monomers reactivity ratios in radical polymerizations. Four quantum chemical descriptors based on density functional theory (DFT) calculations were used to develop the ANN model. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network 4-4-1, the results show that the predicted parameter π values are in good agreement with the experimental ones, with the root mean square (rms) errors being 0.053 (R=0.960) for the training set and 0.070 (R=0.942) for the test set. The ANN model has better statistic quality than the MLR model, which indicates there are nonlinear relationships between these quantum chemical descriptors and the parameter π.

scholarly journals Kernel Methods for Predicting Yields of Chemical Reactions

2021 ◽  
Author(s):  
Alexe Haywood ◽  
Joseph Redshaw ◽  
Magnus Hanson-Heine ◽  
Adam Taylor ◽  
Alex Brown ◽  
...  
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Aryl Halide ◽  
Reaction Yield ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Molecular Fingerprints ◽  
Chemical Descriptors ◽  
Synthetic Assessment

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.

scholarly journals Kernel Methods for Predicting Yields of Chemical Reactions

2021 ◽  
Author(s):  
Alexe Haywood ◽  
Joseph Redshaw ◽  
Magnus Hanson-Heine ◽  
Adam Taylor ◽  
Alex Brown ◽  
...  
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Aryl Halide ◽  
Reaction Yield ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Molecular Fingerprints ◽  
Chemical Descriptors ◽  
Synthetic Assessment

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.

scholarly journals 2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

2019 ◽  
Vol 3 (3) ◽  
pp. 179-186
Author(s):  
Assia Belhassan ◽  
Samir Chtita ◽  
Tahar Lakhlifi ◽  
Mohammed Bouachrine
Keyword(s):  
Quantum Chemical ◽  
Density Functional ◽  
Training Set ◽  
Quantum Chemical Descriptors ◽  
Qspr Model ◽  
Functional Theory ◽  
The Density Functional Theory ◽  
Chemical Descriptors ◽  
External Consistency ◽  
And Training

In this study, we have established two-dimensional quantitative structure propriety relationships (2D-QSPR) model, for a group of 78 molecules based on pyrazine, these molecules were subjected to a 2D-QSPR analyze for their odors thresholds propriety using stepwise Multiple Linear Regression (MLR). The 35 parameters are calculated for the 78 studied compounds using the Gaussian 09W, ChemOffice and ChemSketch softwares. Quantum chemical calculations are used to calculate electronic and quantum chemical descriptors, using the density functional theory (B3LYP/6-31G (d) DFT) methods.The model was used to predict the odors thresholds propriety of the test and training set compounds, and the statistical results exhibited high internal and external consistency as demonstrated by the validation methods.

Artificial neural network prediction of steric hindrance parameter of polymers

2009 ◽  
Vol 63 (4) ◽  
Author(s):  
Xinliang Yu ◽  
Wenhao Yu ◽  
Bing Yi ◽  
Xueye Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Steric Hindrance ◽  
Density Functional ◽  
Basis Set ◽  
Ann Model ◽  
Quantum Chemical Descriptors ◽  
Neural Network Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractAn artificial neural network (ANN) model for modeling and prediction of the steric hindrance parameter σ of polymers with three quantum chemical descriptors, the average polarizability of a molecule α, entropy S, and dipole moment μ, was developed. These descriptors were calculated from the monomers of the respective polymers according to the density functional theory at the B3LYP level of the theory with the 6-31G(d) basis set. Optimal conditions were obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [3-1-1], the results show that the predicted σ values are in good agreement with the experimental ones, with the root mean square (rms) error being 0.080 (R = 0.945) for the training set, and 0.078 (R = 0.918) for the test set, which indicates that the proposed model has better predictive capability than the existing one.

scholarly journals Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

2020 ◽  
Vol 21 (6) ◽  
pp. 2053 ◽  
Author(s):  
Bogusław Buszewski ◽  
Petar Žuvela ◽  
Gulyaim Sagandykova ◽  
Justyna Walczak-Skierska ◽  
Paweł Pomastowski ◽  
...  
Keyword(s):  
Electrostatic Interactions ◽  
Density Functional ◽  
Stationary Phases ◽  
Retention Mechanism ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Functional Theory ◽  
Dispersion Effects ◽  
Chemical Descriptors ◽  
Structure Retention

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical ‘black box’ approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

scholarly journals Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

2008 ◽  
Vol 6 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Gui-Ning Lu ◽  
Xue-Qin Tao ◽  
Zhi Dang ◽  
Xiao-Yun Yi ◽  
Chen Yang
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Quantum Chemical ◽  
Partition Coefficients ◽  
Density Functional ◽  
Optimal Model ◽  
Quantum Chemical Descriptors ◽  
Polycyclic Aromatic ◽  
Pls Analysis ◽  
Chemical Descriptors

AbstractQuantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

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