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Transition state-finding strategies for use with the growing string method

机译:用于增长字符串方法的过渡状态发现策略

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Efficient identification of transition states is important for understanding reaction mechanisms. Mosttransition state search algorithms require long computational times and a good estimate of thetransition state structure in order to converge, particularly for complex reaction systems. Thegrowing string method (GSM) [B. Peters et al., J. Chem. Phys. 120, 7877 (2004)] does not requirean initial guess of the transition state; however, the calculation is still computationally intensive dueto repeated calls to the quantum mechanics code. Recent modifications to the GSM [A. Goodrow etal., J. Chem. Phys. 129, 174109 (2008)] have reduced the total computational time for convergingto a transition state by a factor of 2 to 3. In this work, three transition state-finding strategies havebeen developed to complement the speedup of the modified-GSM: (1) a hybrid strategy, (2) anenergy-weighted strategy, and (3) a substring strategy. The hybrid strategy initiates the stringcalculation at a low level of theory (HF/STO-3G), which is then refined at a higher level of theory(B3LYP/6-31G~*). The energy-weighted strategy spaces points along the reaction pathway based onthe energy at those points, leading to a higher density of points where the energy is highest and finerresolution of the transition state. The substring strategy is similar to the hybrid strategy, but only aportion of the low-level string is refined using a higher level of theory. These three strategies havebeen used with the modified-GSM and are compared in three reactions: alanine dipeptideisomerization, H-abstraction in methanol oxidation on VO_X/SiO_2catalysts, and C–H bondactivation in the oxidative carbonylation of toluene to p-toluic acid on Rh(CO)_2(TFA)_3catalysts. Ineach of these examples, the substring strategy was proved most effective by obtaining a betterestimate of the transition state structure and reducing the total computational time by a factor of 2to 3 compared to the modified-GSM. The applicability of the substring strategy has been extendedto three additional examples: cyclopropane rearrangement to propylene, isomerization ofmethylcyclopropane to four different stereoisomers, and the bimolecular Diels–Alder condensationof 1,3-butadiene and ethylene to cyclohexene. Thus, the substring strategy used in combination withthe modified-GSM has been demonstrated to be an efficient transition state-finding strategy for awide range of types of reactions.
机译:高效识别过渡态对于理解反应机理很重要。大多数过渡状态搜索算法需要较长的计算时间和对过渡状态结构的良好估计才能收敛,特别是对于复杂的反应系统。生长字符串法(GSM)[B.彼得斯等,化学杂志。物理120,7877(2004)]不需要对过渡状态进行初步猜测;但是,由于重复调用量子力学代码,因此计算仍然需要大量计算。对GSM的最新修改[A. Goodrow等,化学杂志。物理[129,174109(2008)]将收敛到过渡态的总计算时间减少了2到3倍。在这项工作中,已经开发了三种过渡态发现策略来补充改进的GSM的加速:(1 )混合策略,(2)能源加权策略和(3)子串策略。混合策略以较低的理论水平(HF / STO-3G)启动字符串计算,然后以较高的理论水平(B3LYP / 6-31G〜*)进行细化。能量加权策略根据这些点上的能量沿反应路径分配点,从而导致能量最高的点的密度更高,并且过渡态的分辨率更高。子字符串策略与混合策略相似,但是使用较高级别的理论仅精炼一部分低级字符串。这三种策略已与修饰的GSM一起使用,并在以下三个反应中进行了比较:丙氨酸二肽异构化,VO_X / SiO_2催化剂上甲醇氧化中的H-抽象,以及甲苯在Rh(Rh( CO)_2(TFA)_3催化剂。在每个示例中,与改进的GSM相比,通过获得过渡状态结构的最佳估计并将总计算时间减少2到3倍,证明了子串策略最有效。子串策略的适用性已扩展到另外三个例子:环丙烷重排成丙烯,甲基环丙烷异构化成四个不同的立体异构体以及双分子Diels-Alder缩合的1,3-丁二烯和乙烯成环己烯。因此,已证明与改进的GSM结合使用的子串策略是针对多种反应类型的有效过渡态发现策略。

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