二元无机物标准熵 So298的拓扑研究

为了预测二元无机物的标准熵，基于分子图的连接矩阵和离子参数gi、qi，提出了一种新的连接性指数mQ, mG及其逆指数mQ’, mG’。 qi、gi定义为：qi=(1.1+Zi1.1) /(1.7+ni), gi=(1.4+Zi) /(0.9+ri+ri-1),其中Zi 、ni和 ri分别代表离子i的电荷数、最外层主量子数和半径。从0Q, 0Q’, 1G,和1G’,利用多元线性回归分析方法和人工神经网络方法，可以构建优良的QSPR模型。对371个二元无机物，其多元线性模型及神经网络模型的相关系数、标准偏差和平均绝对偏差分别是：0.9905, 8.29 J.K-1.mol-1, 6.48 J. K-1.mol-1, 0.9960, 5.37 J.K-1.mol-1 和 3.90 J.K-1.mol-1。留一法交叉验证表明，其多元线性模型具有良好的稳定性。两个模型对187个未进入模型的二元无机物的标准熵的预测值和实验值之间的相关系数、标准偏差和平均绝对偏差分别是：0.9897, 8.64 J. K-1.mol-1, 6.84 J. K-1.mol-1, 0.9957, 5.63 J.K-1.mol-1, 和 4.18 J.K-1.mol-1。研究表明，本文方法在预测二元无机物标准熵时比文献方法更有效，两种模型均能较精确的预测二元无机物的标准熵，且神经网络模型的预测结果更精确。

Topological Research on Standard Absolute Entropies,S⊖298, for Binary Inorganic Compounds

For predicting the standard entropy of a binary inorganic compound, two novel connectivity indexes ^mQ, ^mG and their converse indexes ^mQ', ^mG' based on adjacency matrix of molecular graphs and ionic parameters g_i, q_i were proposed. The q_i and g_i are defined as q_i=(1.1+Z_i^1.1)/(1.7+n_i), g_i=(1.4+Z_i)/(0.9+r_i+r_i^?1), where Z_i, n_i, r_i are the charge numbers, the outer electronic shell primary quantum numbers, and the radii of ionic i respectively. The good Quantitative Structure‐Property Relationship (QSPR) models for the standard entropies of binary inorganic compound can be constructed from ⁰Q, ⁰Q', ¹G, and ¹G', by using a multivariate linear regression (MLR) method and an artificial neural network (NN) method. The correlation coefficient r, the standard error s, and the average absolute deviation of the MLR model and the NN model are 0.9905, 8.29 J·K^?1·mol^?1 and 6.48 J·K^?1·mol^?1, and 0.9960, 5.37 J·K^?1·mol^?1 and 3.90 J·K^?1·mol^?1, respectively, for 371 binary inorganic compounds (training set). The cross‐validation by using the leave‐one‐out method demonstrates that the MLR model is highly reliable from the point of view of statistics. The correlation coefficients, standard deviations and average absolute deviations of predicted values of the standard entropies of other 185 binary inorganic compounds (test set) are 0.9897, 8.64 J·K^?1· mol^?1 and 6.84 J·K^?1·mol^?1, and 0.9957, 5.63 J·K^?1·mol^?1 and 4.18 J·K^?1·mol^?1 for the MLR model and the NN model, respectively. The results show that the current method is more effective than literature methods for estimating the standard entropy of a binary inorganic compound. Both MLR and NN methods can provide acceptable models for the prediction of the standard entropies of binary inorganic compounds. The NN model for the standard entropies appears to be more reliable than the MLR model.