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An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-05-09 , DOI: 10.1021/acs.jcim.1c01570 Wei-Hua Yang 1 , Ya-Meng Li 1 , Jian-Xiang Bi 1 , Rao Huang 1 , Gui-Fang Shao 2 , Tian-E Fan 3 , Tun-Dong Liu 2 , Yu-Hua Wen 1
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-05-09 , DOI: 10.1021/acs.jcim.1c01570 Wei-Hua Yang 1 , Ya-Meng Li 1 , Jian-Xiang Bi 1 , Rao Huang 1 , Gui-Fang Shao 2 , Tian-E Fan 3 , Tun-Dong Liu 2 , Yu-Hua Wen 1
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Global optimization of multicomponent cluster structures is considerably time-consuming due to the existence of a vast number of isomers. In this work, we proposed an improved self-adaptive differential evolution with the neighborhood search (SaNSDE) algorithm and applied it to the global optimization of bimetallic cluster structures. The cross operation was optimized, and an improved basin hopping module was introduced to enhance the searching efficiency of SaNSDE optimization. Taking (PtNi)N (N = 38 or 55) bimetallic clusters as examples, their structures were predicted by using this algorithm. The traditional SaNSDE algorithm was carried out for comparison with the improved SaNSDE algorithm. For all the optimized clusters, the excess energy and the second difference of the energy were calculated to examine their relative stabilities. Meanwhile, the bond order parameters were adopted to quantitatively characterize the cluster structures. The results reveal that the improved SaNSDE algorithm possessed significantly higher searching capability and faster convergence speed than the traditional SaNSDE algorithm. Furthermore, the lowest-energy configurations of (PtNi)38 clusters could be classified as the truncated octahedral and disordered structures. In contrast, all the optimal (PtNi)55 clusters were approximately icosahedral. Our work fully demonstrates the high efficiency of the improved algorithm and advances the development of global optimization algorithms and the structural prediction of multicomponent clusters.
中文翻译：
一种改进的基于邻域搜索算法的自适应差分进化，用于双金属簇全局优化
由于存在大量异构体，多组分簇结构的全局优化非常耗时。在这项工作中，我们提出了一种改进的自适应差分进化与邻域搜索（SaNSDE）算法，并将其应用于双金属簇结构的全局优化。优化了交叉运算，并引入了改进的盆地跳跃模块，以提高SaNSDE优化的搜索效率。取 (PtNi) N ( N= 38 或 55）双金属簇作为例子，它们的结构是通过使用该算法进行预测的。将传统的SaNSDE算法与改进的SaNSDE算法进行比较。对于所有优化的簇，计算剩余能量和能量的二次差以检查它们的相对稳定性。同时，采用键序参数对簇结构进行定量表征。结果表明，改进的SaNSDE算法比传统的SaNSDE算法具有明显更高的搜索能力和更快的收敛速度。此外，(PtNi) 38簇的最低能量构型可归类为截断八面体和无序结构。相比之下，所有最优 (PtNi) 55簇大致呈二十面体。我们的工作充分展示了改进算法的高效性，推动了全局优化算法的发展和多组分簇的结构预测。
更新日期：2022-05-09
中文翻译：
一种改进的基于邻域搜索算法的自适应差分进化，用于双金属簇全局优化
由于存在大量异构体，多组分簇结构的全局优化非常耗时。在这项工作中，我们提出了一种改进的自适应差分进化与邻域搜索（SaNSDE）算法，并将其应用于双金属簇结构的全局优化。优化了交叉运算，并引入了改进的盆地跳跃模块，以提高SaNSDE优化的搜索效率。取 (PtNi) N ( N= 38 或 55）双金属簇作为例子，它们的结构是通过使用该算法进行预测的。将传统的SaNSDE算法与改进的SaNSDE算法进行比较。对于所有优化的簇，计算剩余能量和能量的二次差以检查它们的相对稳定性。同时，采用键序参数对簇结构进行定量表征。结果表明，改进的SaNSDE算法比传统的SaNSDE算法具有明显更高的搜索能力和更快的收敛速度。此外，(PtNi) 38簇的最低能量构型可归类为截断八面体和无序结构。相比之下，所有最优 (PtNi) 55簇大致呈二十面体。我们的工作充分展示了改进算法的高效性，推动了全局优化算法的发展和多组分簇的结构预测。
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