Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression.,Journal of Chemical Theory and Computation

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Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression.
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2020-07-01 , DOI: 10.1021/acs.jctc.0c00348
Alexander Denzel ₁ , Johannes Kästner ₁

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We show how Gaussian process regression can be used to update Hessian matrices using gradient-based information in the course of an optimization procedure. This is done by building a Gaussian process with at least one initial Hessian and some further energies and gradients from electronic structure calculations and evaluating the desired second derivative of the resulting Gaussian process. To a certain extent, we can overcome the significant scaling problems that occur when training a Gaussian process with Hessian information. We demonstrate in benchmark runs using the partitioned rational function optimization (P-RFO) that this new update method can outperform classical Hessian update methods for small systems.

中文翻译：

基于高斯过程回归的过渡状态搜索的Hessian矩阵更新方案。

我们展示了如何在优化过程中使用基于梯度的信息将高斯过程回归用于更新Hessian矩阵。这是通过使用电子结构计算中的至少一个初始Hessian以及一些其他能量和梯度建立高斯过程并评估所得高斯过程的所需二阶导数来完成的。在一定程度上，我们可以克服在使用Hessian信息训练高斯过程时出现的重大缩放问题。我们在使用分区有理函数优化（P-RFO）的基准测试中证明，这种新的更新方法可以优于小型系统的经典Hessian更新方法。

更新日期：2020-08-11

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