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.     

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     

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。     

链烷烃标准熵的拓扑研究

根据核磁共振的峰数及Randic的碳原子支化度,提出了碳原子特征值.基于连接矩阵和碳原子特征值,建立新的连接指数 mY.其中的0阶连接性指数 0Y很容易计算,且对烷烃异构体有很强的区分能力.将157种气态链烷烃的标准熵与其 0Y相关联, R=0.9985, 属于优级模型.与Randic指数的 0χ及倪才华等提出的信息拓扑指数Ix、 IW比较, 0Y具有良好的性质相关性与结构选择性.     

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     

信息拓扑指数与烷烃分子热力学性质的关系

Two topological information indices were constructed based on Randic and Wiener indices, and the values of topological information indices for 85 alkanes were calculated. The thermodynamic properties such as the standard enthalpies of formation, the standard entropies and the standard free energies of formation for these alkanes were also correlated with these topological and information indices. It is found that the thermodynamic properties calculated for both gaseous and liquid states of the 85 alkanes are in excellent agreement with the experimental values through the regression analysis.     

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.     

烷烃的热力学性质与结构的关系

在分子图邻接矩阵的基础上提出了一个新的连接性指数mX,mX与烷烃的标准熵、原子化焓、标准生成焓、汽化焓、标准生成吉布斯自由能具有良好的线性关系,相关系数均在0·99以上。结果表明,该模型简单、实用、可靠,而且物理意义明确,对有机物有较高的结构区分能力。对157种烷烃的计算结果表明,热力学性质的计算值和实验值的平均相对误差不超过0·77%。     

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.     

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF 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.     

Experimental study on the 3D‐printed plastic parts and predicting the mechanical properties using artificial neural networks

This study investigates the mechanical properties of 3D‐printed plastic parts fabricated using Fused Deposition Modeling (FDM). For this purpose, a 3D printer named KASAME was designed and built by the researchers. The test samples were fabricated using polylactic acid (PLA). The experiments were conducted using three melt temperatures (190°C, 205°C, and 220°C), four layer thickness values (0.06 mm, 0.10 mm, 0.19 mm, and 0.35 mm), and three raster pattern orientations (+45°/?45° [the crisscross pattern], horizontal and vertical). Tensile strength tests were performed to determine tensile strength values of the samples and fracture surfaces were also analyzed. Using artificial neural networks, a mathematical model for the tensile test results was generated corresponding to the raster pattern employed in 3D fabrication. Tensile strength tests indicated that melt temperature, layer thickness, and raster pattern orientation had a significant effect on the tensile strengths of the samples. According to the result of the experiment, the maximum average tensile strength values were observed for the samples fabricated using the crisscross raster pattern. The analysis of variance (ANOVA) table shows the raster pattern (PCR) value of 48.68% was obtained with the highest degree of influence. With respect to R ², the best performing artificial neural network model, with test and training values of 0.999199 and 0.999997, respectively, was observed to be the crisscross raster pattern. Copyright © 2016 John Wiley & Sons, Ltd.