Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF

We have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. While generating the trial geometries, a Tabu list is used for storing the information of the already used trial geometries to avoid using the similar trial geometries. In this recursive algorithm, an n-sized cluster is built from the geometries of n−1 clusters. The overall procedure automatically generates many unique minimum energy geometries of clusters with size from 2 up to n using this evolutionary growth strategy. We have used our strategy on some of the well-studied clusters such as Pd, Pt, Au, and Al homometallic clusters, Ru-Pt and Au-Pt binary clusters, and Ag-Au-Pt ternary cluster. We have analyzed some of the popular parameters to characterize the clusters, such as relative energy, singlet-triplet energy difference, binding energy, second-order energy difference, and mixing energy, and compared with the reported properties.

本研究团队已研发出一套算法，旨在自动构建纳米簇的全局最小能级及其他低能级最小能级。该算法已嵌入至PyAR（https://github.com/anooplab/pyar）程序之中。PyAR中的全局优化过程分为两个部分：多个试探几何形的生成以及基于梯度的试探几何形局部优化。在生成试探几何形的过程中，采用禁忌列表来存储已使用过的试探几何形信息，以此规避重复使用相似试探几何形。在本递归算法中，通过将n-1个簇的几何形组合构建出一个n大小的簇。整体流程通过这种进化增长策略，自动生成从2至n大小范围内的多个独特的最小能量几何形。本研究策略已应用于一些研究较为深入的簇，例如Pd、Pt、Au和Al的同金属簇，Ru-Pt和Au-Pt二元簇，以及Ag-Au-Pt三元簇。针对表征簇的一些流行参数，如相对能量、单重态-三重态能量差、结合能、二阶能量差和混合能等，我们进行了分析，并将分析结果与已报道的性质进行了比较。