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Search for the most stable structures on potential energy surfaces

机译:在势能表面上寻找最稳定的结构

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Smoothing techniques for global optimization in search for the most stable structures (clusters or conformers) have been a novel possibility for the last decade. The techniques turned out to be related to a variety of fundamental laws: Fick's diffusion equation, time-dependent and time-independent Schrodinger equations, Smoluchowski dynamics equation, Bloch equation of canonical ensemble evolution with temperature, Gibbs free-energy principle. The progress indicator of global optimization in those methods takes different physical meanings: time, imaginary time, Planck constant, or the inverse absolute temperature. Despite this large spectrum of physical phenomena, the resulting global optimization procedures have a remarkable common feature. In the case of the Gaussian Ansatz for the wave function or density distribution, the underlying differential equations of motion for the Gaussian position and width are similar for all these phenomena. In all techniques the smoothed potential energy function plays a central role rather than the potential energy function itself. The smoothed potential results from a Gaussian convolution or filtering out high frequency Fourier components of the original potential energy function. During the minimization, the Gaussian position moves according to the negative gradient of the smoothed potential energy function. The Gaussian width is position dependent through the curvature of the potential energy function, and evolves according to the following rule. For sufficiently positive curvatures (close to minima of the smoothed potential) the width decreases, thus leading to a smoothed potential approaching the original potential energy function, while for negative curvatures (close to maxima) the width increases leading eventually to the disappearance of humps of the original potential energy function. This allows for crossing barriers separating the energy basins. Some methods result in an additional term, which increases the width, when the potential becomes flat. This may be described as a feature allowing hunting for distant minima. [References: 33]
机译:在最近十年中,用于全局最优化以寻找最稳定的结构(簇或构象异构体)的平滑技术是一种新的可能性。事实证明,这些技术与各种基本定律有关:Fick扩散方程,与时间和时间无关的Schrodinger方程,Smoluchowski动力学方程,随温度的典范整体演化的Bloch方程,Gibbs自由能原理。在这些方法中,全局优化的进度指示器具有不同的物理含义:时间,虚拟时间,普朗克常数或逆绝对温度。尽管存在大量的物理现象,但最终的全局优化过程仍具有明显的共同特征。对于波函数或密度分布的高斯Ansatz,对于所有这些现象,高斯位置和宽度的基本运动微分方程都是相似的。在所有技术中,平滑的势能函数起着核心作用,而不是势能函数本身。平滑后的势由高斯卷积或滤除原始势能函数的高频傅立叶分量产生。在最小化期间​​,高斯位置根据平滑的势能函数的负梯度移动。高斯宽度是由势能函数的曲率决定的位置,并根据以下规则发展。对于足够大的正曲率(接近平滑电势的最小值),宽度减小,从而导致平滑的电势接近原始势能函数;而对于负曲率(接近最大值),宽度增大,最终导致峰的驼峰消失。原始势能函数。这允许跨越障碍分隔能量盆。一些方法会导致附加项,当电势变平时,它会增加宽度。这可以描述为允许寻找遥远的最小值的特征。 [参考:33]

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