We found an alternative method for the derivation of transition state structure energy in chemical reactions which would be less dependent on the starting geometry of reactants by combining a mathematical tool and artificial neural networks(ANN)with conventional transition state optimization algorithms.When two reactants approach each other,every geometric structure corresponds to a system energy value.The purpose of this investigation was to collect as many energy values on the reaction energy surface as possible.By simulating the energy surface using the geometric parameters as independent variables,the first order saddle point in the energy surface corresponding to the transition state slyucture was derived.The nucleophilic attack step of a classical Aldol reaction was studied using acetaldehyde anion and formaldehyde as reactants.The intrinsic reaction coordinate(IRC)path calculation started with 3 different sets of starting reactant geometries and 96 points on the reaction energy surface were derived.The energy surface Was simulated using ANN.Cross-validation was applied to evaluate the result and avoided a possible overfilling of the ANN.%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最值.本研究的目的是尽町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.
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