CAJ | 학술논문

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.
통과종합사용전통적과도태우화산법、수학통계공구이급인공신경망락산법(ANN)조도일충불의뢰우반응물기시구상이득도화학반응중과도태결구화능량적방법.재량개반응물호상접근적과정중,매일보적궤하구상도대응착일개계통능최치.본연구적목적시진정능지수집처재반응능량면상적저충능량점치.통과채용궤하삼수작위자변량대세능면진행모의연구,득도료세능면상대응과도태결구적일계안점.채용을철부리자화갑철작위반응물,대경전적철순축합반응중적친핵진공보취진행료연구.대내품반응좌표(IRC)로경적계산시종반응물적삼조불동기시구상출발,최종획득료반응세능면상적96개점.본연구중적세능면채용인공신경망락산법진행모의연구,병이용교차험증방법평고득도적결과,피면료채용인공신경망락산법시과도의합정황적발생.
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.