论文标题
过渡状态搜索的高斯流程回归
Gaussian Process Regression for Transition State Search
论文作者
论文摘要
我们实施了一种基于梯度的算法，用于过渡状态搜索，该算法使用高斯流程回归。除了对算法的描述外，我们还提供了一种方法，以找到优化的起点，如果仅知道反应物和产物最小值。我们针对DL-Find库中实现的二聚体方法和分区的有理函数优化对27个测试系统进行基准测试。我们发现新的优化器可显着减少所需的能量和梯度评估的数量。
We implemented a gradient-based algorithm for transition state search which uses Gaussian process regression. Besides a description of the algorithm, we provide a method to find the starting point for the optimization if only the reactant and product minima are known. We perform benchmarks on 27 test systems against the dimer method and partitioned rational function optimization as implemented in the DL-FIND library. We found the new optimizer to significantly decrease the number of required energy and gradient evaluations.
