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通过综合使用传统的过渡态优化算法、数学统计工具以及人工神经网络算法(ANN)找到一种不依赖于反应物起始构象而得到化学反应中过渡态结构和能量的方法.在两个反应物互相接近的过程中,每一步的几何构象都对应着一个系统能量值.本研究的目的是尽可能地收集处在反应能量面上的这种能量点值.通过采用几何参数作为自变量对势能面进行模拟研究,得到了势能面上对应过渡态结构的一阶鞍点.采用乙醛负离子和甲醛作为反应物,对经典的醛醇缩合反应中的亲核进攻步骤进行了研究.对内禀反应坐标(IRC)路径的计算是从反应物的三组不同起始构象出发,最终获得了反应势能面上的96个点.本研究中的势能面采用人工神经网络算法进行模拟研究,并利用交叉验证方法评估得到的结果,避免了采用人工神经网络算法时过度拟合情况的发生.
Through the comprehensive use of the traditional transition state optimization algorithm, mathematical statistics tools and artificial neural network algorithm (ANN) to find a way to obtain the chemical structure of the transition state and energy independent of the reactant initial conformation in the two reactants In the process of getting close to each other, the geometric conformation of each step corresponds to a system energy value.The purpose of this study is to collect as much as possible the energy point value on the reaction energy surface.By using the geometric parameters as the independent variable, Surface, the first-order saddle point corresponding to the transition state structure on the potential energy surface was obtained.The nucleophilic attack steps in the classic aldol condensation reaction were studied by using acetaldehyde anion and formaldehyde as reactants.The effects of intrinsic reaction The IRC path is calculated from three different starting conformations of the reactants, and finally 96 points of the reaction potential energy surface are obtained. The potential energy surface in this study is simulated by the artificial neural network algorithm, The result obtained by the verification method evaluation avoids the occurrence of overfitting when using the artificial neural network algorithm.