Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function.,Journal of Chemical Theory and Computation

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Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function.
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2019-12-19 , DOI: 10.1021/acs.jctc.9b01038
Olli-Pekka Koistinen ₁ , Vilhjálmur Ásgeirsson ₁ , Aki Vehtari , Hannes Jónsson ₁

Affiliation

The minimum mode following method can be used to find saddle points on an energy surface by following a direction guided by the lowest curvature mode. Such calculations are often started close to a minimum on the energy surface to find out which transitions can occur from an initial state of the system, but it is also common to start from the vicinity of a first-order saddle point making use of an initial guess based on intuition or more approximate calculations. In systems where accurate evaluations of the energy and its gradient are computationally intensive, it is important to exploit the information of the previous evaluations to enhance the performance. Here, we show that the number of evaluations required for convergence to the saddle point can be significantly reduced by making use of an approximate energy surface obtained by a Gaussian process model based on inverse interatomic distances, evaluating accurate energy and gradient at the saddle point of the approximate surface and then correcting the model based on the new information. The performance of the method is tested with start points chosen randomly in the vicinity of saddle points for dissociative adsorption of an H2 molecule on the Cu(110) surface and three gas phase chemical reactions.

中文翻译：

使用具有反距离协方差函数的高斯过程回归进行最小模式鞍点搜索。

通过遵循由最低曲率模式引导的方向，可以使用最小模式跟随方法在能量表面上找到鞍点。通常从能量表面上的最小值开始进行此类计算，以找出可能从系统的初始状态发生的跃迁，但是通常也可以使用初始值从一阶鞍点附近开始进行计算。根据直觉或更近似的计算进行猜测。在能量和其梯度的准确评估需要大量计算的系统中，重要的是利用先前评估的信息来增强性能。这里，我们表明，通过使用高斯过程模型基于逆原子间距离获得的近似能量表面，评估近似鞍点处的准确能量和梯度，可以显着减少收敛到鞍点所需的评估次数。表面，然后根据新信息校正模型。该方法的性能是通过在鞍点附近随机选择的起点进行测试的，该起点用于H2分子在Cu（110）表面的解离吸附和三个气相化学反应。

更新日期：2019-12-20

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