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A theoretical study in extracting the essential features and dynamics of molecular motions: Intrinsic geometry methods for PF(5) pseudorotations and statistical methods for argon clusters.

机译:提取分子运动的基本特征和动力学的理论研究:PF(5)伪旋转的本征几何方法和氩簇的统计方法。

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We studied the problem of understanding and computing the essential features and dynamics of molecular motions through the development of two theories for two different systems. First, we studied the process of the Berry Pseudorotation of PF5 and the rotations it induces in the molecule through its natural and intrinsic geometric nature by setting it in the language of fiber bundles and graph theory. With these tools, we successfully extracted the essentials of the process' loops and induced rotations. The infinite number of pseudorotation loops were broken down into a small set of essential loops called "super loops", with their intrinsic properties and link to the physical movements of the molecule extensively studied. In addition, only the three "self-edge loops" generated any induced rotations, and then only a finite number of classes of them. Second, we studied applying the statistical methods of Principal Components Analysis (PCA) and Principal Coordinate Analysis (PCO) to capture only the most important changes in Argon clusters so as to reduce computational costs and graph the potential energy surface (PES) in three dimensions respectively. Both methods proved successful, but PCA was only partially successful since one will only see advantages for PES database systems much larger than those both currently being studied and those that can be computationally studied in the next few decades to come. In addition, PCA is only needed for the very rare case of a PES database that does not already include Hessian eigenvalues.
机译:通过针对两个不同系统的两种理论的发展,我们研究了理解和计算分子运动的基本特征和动力学的问题。首先,我们通过将其设置为纤维束和图论的语言,研究了PF5的Berry伪旋转过程及其在分子中的自然和内在几何性质引起的旋转。使用这些工具,我们成功地提取了过程循环和引起旋转的要素。无限数量的伪旋转环被分解为一小组称为“超级环”的基本环,这些环具有其固有特性并与广泛研究的分子的物理运动有关。另外,只有三个“自边缘循环”产生了任何诱导的旋转,然后仅产生了有限数量的类别。其次,我们研究了应用主成分分析(PCA)和主坐标分析(PCO)的统计方法仅捕获氩团簇中最重要的变化,从而减少了计算成本并绘制了三维的势能面(PES)分别。两种方法都被证明是成功的,但是PCA仅取得了部分成功,因为人们只会看到PES数据库系统的优势远大于目前正在研究的系统和可以在未来几十年进行计算研究的系统。此外,仅在非常罕见的PES数据库(不包含Hessian特征值)的情况下才需要PCA。

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