基于模态跟踪的驻点优化
Mode-Tracking Based Stationary-Point Optimization
摘要：
在这项工作中，我们提出了一个过渡状态优化协议的基础上的模式跟踪算法[J.化学物理。118（2003）1634]。通过仅计算感兴趣的特征向量而不是对角化完整的Hessian矩阵并基于选择性计算的向量执行特征向量跟随搜索，我们可以有效地优化过渡态结构。初始猜测结构和本征向量是从反应物和产物结构之间的线性插值、轻推弹性带搜索、约束优化扫描或最小能量结构中选择的。或者，可以定义基于化学直觉的初始猜测向量。然后，我们迭代地完善所选择的矢量的戴维森子空间迭代技术。这个过程加速了寻找几百个原子的大分子的过渡态。在起始结构与过渡态结构非常不同的情况下，或者在期望的向量不是具有最低特征值的向量的情况下，这也是有益的。探索性研究的反应途径是可行的，通过以下手动构建的分子扭曲。
英文摘要：
In this work, we present a transition-state optimization protocol based on the Mode-Tracking algorithm [J. Chem. Phys. 118 (2003) 1634]. By calculating only the eigenvector of interest instead of diagonalizing the full Hessian matrix and performing an eigenvector following search based on the selectively calculated vector, we can efficiently optimize transition-state structures. The initial guess structures and eigenvectors are either chosen from a linear interpolation between the reactant and product structures, from a nudged-elastic band search, from a constrained-optimization scan, or from the minimum-energy structures. Alternatively, initial guess vectors based on chemical intuition may be defined. We then iteratively refine the selected vectors by the Davidson subspace iteration technique. This procedure accelerates finding transition states for large molecules of a few hundred atoms. It is also beneficial in cases where the starting structure is very different from the transition-state structure or where the desired vector to follow is not the one with lowest eigenvalue. Explorative studies of reaction pathways are feasible by following manually constructed molecular distortions.
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