利用高斯过程高效发现多个最小action通路
Efficient discovery of multiple minimum action pathways using Gaussian process
摘要：
提出了一种基于最小作用量原理和高斯过程回归方法的过渡路径搜索方法。迄今为止开发的大多数路径搜索方法都依赖于字符串表示，它通过一系列缓慢变化的系统副本来近似过渡路径。这样的弦方法通常在计算上是昂贵的，因为它们需要许多副本来获得平滑的路径。在这里，我们提出了一种方法，采用高斯过程回归方法，推断的势能面的形状与一些观察到的数据和高斯形核函数。我们证明了一个大幅度提高的计算效率的方法约五个数量级比现有的方法。此外，为了证明其现实世界的能力，我们应用我们的方法来找到多个构象转换途径的丙氨酸二肽使用量子力学势。由于高斯过程作用量优化（GPAO）方法效率的提高，我们得到了丙氨酸二肽的多重跃迁途径，并成功地用从头算的方法计算了它们的跃迁几率.此外，GPAO还成功地发现了小分子的异构化途径和金属表面原子的重排。
英文摘要：
We present a new efficient transition pathway search method based on the least action principle and the Gaussian process regression method. Most pathway search methods developed so far rely on string representations, which approximate a transition pathway by a series of slowly varying system replicas. Such string methods are computationally expensive in general because they require many replicas to obtain smooth pathways. Here, we present an approach employing the Gaussian process regression method, which infers the shape of a potential energy surface with a few observed data and Gaussian-shaped kernel functions. We demonstrate a drastic elevation of computing efficiency of the method about five orders of magnitude than existing methods. Further, to demonstrate its real-world capabilities, we apply our method to find multiple conformational transition pathways of alanine dipeptide using a quantum mechanical potential. Owing to the improved efficiency of our method, Gaussian process action optimiza tion (GPAO), we obtain the multiple transition pathways of alaninedipeptide and calculate their transition probabilities successfully with ab initio accuracy. In addition, GPAO successfully finds the isomerization pathways of small molecules and the rearrangement of atoms on a metallic surface.
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