分子拓扑指数与链烷烃热力学性质的关系

碳原子个数N和路径数P3是表征链烷烃分子的大小、支化度和形状等结构特征的重要参数,引入烷烃所含甲基数的1.5次方M1和3次方M2表征取代基效应,运用多元线性模型研究了链烷烃标准生成焓、标准摩尔熵、标准生成吉布斯自由能等三种热力学性质与N,P3,M1和M2之间的定量关系,相关系数分别达到0.9993,0.9989和0.9972,标准偏差分别是2.2809kJ/mol,5.9093J/(mol·K),2.0585kJ/mol,其计算值与实验值非常接近.     

用原子特征自相关拓扑指数预测链烷烃的热力学性质

原子特征值(Si)被定义为:.由Si（ ）建构原子特征自相关拓扑指数(F)及原子特征连接性指数(Y)的公式为:F＝∑(Si.Sj)0.5、Y＝∑(Si.Sj)-0.5.它们与85种链烷烃热力学性质(P) 的二元线性回归方程为:P＝a+bF+cY(或P3).P为标准生成焓、标准熵、标准生成自由能的二元相关指数依次为0.9953、0.9992、0.9941,优于文献方法.     

改进的连接性指数用于链烷烃热力学性质与沸点研究

基于邻接矩阵和原子特征值qi，建立邻接指数^mQ,用^0Qr,^1Q与85种链烷烃的标准生成焓、标准生成自由能、标准熵和沸点关联，相关系数均在0.99以上，属于良好模型，与Randic指数的^mX比较，^mQ具有良好的性质相关性。     

用原子特性自相关拓扑指数预测链烷烃的热力学性质

原子特征值 ( Si)被定义为 :Si=ni- 1ki mi( ∑Eij+ hi Ei)。由 Si建构原子特征自相关拓扑指数 ( F )及原子特征连接性指数 ( Y)的公式为 :F =∑( Si· Sj) 0 .5、Y=∑ ( Si·Sj) - 0 .5。它们与 85种链烷烃热力学性质 ( P)的二元线性回归方程为 :P =a + b F +c Y(或 P3) 。 P为标准生成焓、标准熵、标准生成自由能的二元相关指数依次为 0 .9953、0 .9992、0 .9941 ,优于文献方法     

用均根拓扑指数与路径数预测链烷烃的沸点和热力学性质

以距离矩阵为基础，建构调和均根拓扑指数（K），以表征链烷烃分子的大小 和分支情况，85种链烷烃的沸点(T_b)、标准生成焓(Δ_rH_m~θ)、标准熵(S_m~θ )、标准生成自由能(Δ_fG_m~θ)与K及路径数(P_2, P_3)的回归方程为: ln(793- T_b) = 6.48346-0.10092K + 0.00131P_2-0.01110P_3, R = 0.9996; -Δ_fH_m~θ = 62.664 + 25.331 K + 6.597 P_2 - 0.678 P_3, R = 0.9984; S_m~θ = 170. 691 + 67.425 K - 4.712P_2 + 5.251 P_3, R = 0.9989; Δ_fG_m~θ = -45.677 + 10.060 K + 0.555 P_2 + 2.342P_3, R = 0.9935。它们的计算值与相应实验值 都非常吻合。结果表明，K对链烷烃具有良好的结构选择性和性质相关性。     

辨识药物定量构效关系的模糊神经网络方法研究

提出一种基于遗传算法的新型模糊神经网络方法,用于计算Benzodiazepines(BZs)类药物的定量构效关系.这类模糊神经网络综合了神经网络、遗传算法与模糊逻辑的各自优势,具有优良的定量构效关系辨识能力,其学习速度较快,不易陷入局部最小区域;网络知识以模糊语言变量的形式加以表达,不仅易于理解,而且能有效地利用已有的专家经验.一旦通过学习获得规律后,不仅能很好地预测化合物的活性,还能对后续的药物分子设计提供有益的理论指导.     

应用分子电性距离矢量预测烷烃和一元醇的折光指数

应用分子电性距离矢量对81个烷烃、22个一元醇进行了结构表征,通过多元线性回归与逐步回归的方法建立了分子电性距离矢量与折光指数的定量结构性质模型,模型的相关系数分别为0.980和0.979.采用留一法对模型进行交互检验复相关系数R2cv分别为0.927和0.898.说明定量结构性质模型具有很好的稳定性和预测功能.     

有机化合物热力学性质的预测(Ⅲ)--人工神经网络法预测烷烃的生成焓

采用分子子图编码方法将烷烃的分子子图码作为人工神经网络(ANN)的输入参数 ,对烷烃的生成焓进行预测 ,取得了满意的结果 ,其拟合方程的回归系数达到 0 .9845     

利用分子路径指数矢量对烷烃摩尔响应值的估计与预测

 利用分子路径指数矢量表示烷烃分子结构方法 ,结合多元线性回归算法及反传神经网络算法 ,对烷烃摩尔响应值进行处理 ,获得了比文献更佳的预测效果 ,交互校验的相关系数达 0 96 8以上。     

二芳基甲烷衍生物保留因子与分离因子的神经网络理论研究

为了研究手性二芳基甲烷衍生物的保留因子和分离因子,基于分子结构及邻接矩阵,计算了63个手性二芳基甲烷衍生物的分子连接性指数和电性拓扑状态指数。建立了这些分子保留因子、分离因子与优化得到的结构指数之间的相关性模型,并将筛选的结构参数作为BP神经网络的输入层变量,采用不同的网络结构,获得了令人较为满意的三个预测模型,模型的总相关系数R分别为0.981、0.972和0.992。利用模型计算得到的保留因子和分离因子预测值与实验值的平均误差分别为0.041、0.042和0.010,吻合度较好。结果表明手性二芳基甲烷衍生物的保留因子及分离因子与分子结构参数之间有良好的非线性关系。     

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

On the use of computational neural networks for the prediction of polymer properties

A general purpose computational paradigm using neural networks is shown to be capable of efficiently predicting properties of polymeric compounds based on the structure and composition of the monomeric repeat unit. Results are discussed for the prediction of the heat capacity, glass transition temperature, melting temperature, change in the heat capacity at the glass transition temperature, degradation temperature, tensile strength and modulus, ultimate elongation, and compressive strength for 11 different families of polymers. The accuracies of the predictions range from 1–13% average absolute error. The worst results were obtained for the mechanical properties (tensile strength and modulus: 13%, 7% elongation: 12%, and compressive strength: 8%) and the best results for the thermal properties (heat capacity, glass transition temperature, and melting point: <4%). A simple modification to the overall method is devised to better take into account the fact that the mechanical properties are experimentally determined with a fairly large range (due to variability in measurement procedures and especially the sample). This modification treats the bounds on the range for the mechanical properties as complex numbers (complex, modular neural networks) and leads to more rapid optimization with a smaller average error (reduced by 3%).Dedicated to Professor Bernhard Wunderlich on the occasion of his 65th birthdayThis research was sponsored by the Division of Materials Sciences, Office of Basic Energy Sciences, U.S. Department of Energy, under Contract No. DE-AC05-84R21400 with Lockheed Martin Energy Systems, Inc. We would like to express our gratitude for the continued collaboration, support, and interest of Prof. Wunderlich in our research. We would also like to thank participants of the 1st DOE Workshop on Applications of Neural Networks in Materials Sciences for useful discussion on materials properties and neural networks.     

Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter‐propagation artificial neural networks

In this work, the unit cell parameter (a) of the series of cubic ABX₃ perovskites was modeled using counter‐propagation artificial neural networks, and the influence of different input variables was examined by using algorithm for automatic adjustment of the relative importance of the variables. The input variables used in this model were the ionic radii of A, B, and X as well as the oxidation state (z) and the electronegativity (χ) of the anion. The developed models have good generalization performances—good agreement between experimental and predicted values for lattice parameter. One of the important outcomes from this work is obtained from the results of the automatic adjustment of the relative importance of input variables. That is to say, this analysis gave us an insight that the most pronounced influence on the successful prediction of the unit cell parameter of the analyzed data set of cubic ABX₃ perovskites has the effective ionic radii of B‐cation. In addition to this, it may be concluded that the separation of the compounds in different regions of counter‐propagation artificial neural networks was predominantly influenced by the input variables with regard to the physical parameters of the anion. Copyright © 2012 John Wiley & Sons, Ltd.     

Nonlinear mapping of massive data sets by fuzzy clustering and neural networks

Producing good low‐dimensional representations of high‐dimensional data is a common and important task in many data mining applications. Two methods that have been particularly useful in this regard are multidimensional scaling and nonlinear mapping. These methods attempt to visualize a set of objects described by means of a dissimilarity or distance matrix on a low‐dimensional display plane in a way that preserves the proximities of the objects to whatever extent is possible. Unfortunately, most known algorithms are of quadratic order, and their use has been limited to relatively small data sets. We recently demonstrated that nonlinear maps derived from a small random sample of a large data set exhibit the same structure and characteristics as that of the entire collection, and that this structure can be easily extracted by a neural network, making possible the scaling of data set orders of magnitude larger than those accessible with conventional methodologies. Here, we present a variant of this algorithm based on local learning. The method employs a fuzzy clustering methodology to partition the data space into a set of Voronoi polyhedra, and uses a separate neural network to perform the nonlinear mapping within each cell. We find that this local approach offers a number of advantages, and produces maps that are virtually indistinguishable from those derived with conventional algorithms. These advantages are discussed using examples from the fields of combinatorial chemistry and optical character recognition. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 373–386, 2001     

Using artificial neural networks for prediction of organic acid partition coefficients

Aqueous/organic phase partition coefficients of organic acids were predicted using an artificial neural network (ANN) algorithm taking benzoic acid derivatives as examples. The partition coefficients were determined by extraction of the acids from aqueous salt solutions with hydrophilic solvents (BuⁿOH, BuⁱOH, and Bu^tOH). Using the ANN approach makes it possible to obtain quantitative information on the values of the title parameters. Published in Russian in Izvestiya Akademii Nauk. Seriya Khimicheskaya, No. 2, pp. 207—212, February, 2006.     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.