粒子群优化算法在Pt-Pd合金纳米粒子稳定结构优化中的应用

The Application of Particle Swarm Algorithm in the Optimization of Pt-Pd Alloy Nanoparticles Stable Structure

以Pt、Pd为代表的铂族金属在催化剂领域有着广泛的应用,其催化活性较其他金属具有无法比拟的优势,且PtPd合金纳米粒子相对于Pt及Pd单晶纳米粒子均具有更好的催化活性和选择性,研究它的稳定结构对进一步了解催化性能具有重要意义.基于改进粒子群算法和紧束缚二阶近似(TB-SMA)势能函数对不同尺寸、原子数目为1∶1比例的截角八面体Pt-Pd合金纳米粒子的结构稳定性进行了研究,结果表明:改进后的粒子群算法较之前性能得到了提升,能搜索到团簇的更低能量结构,即团簇粒子的更稳定结构;经过改进后,算法更好地克服了早熟收敛的缺点,并且算法的稳定性得到了进一步的改善.

Platinum group metals,represented by Pd and Pt,have been widely used in the field of catalysts,and their catalytic activities hold incomparable advantages compared with other metals.Moreover,Pt-Pd alloyed nanoparticles exhibit better catalytic activity and selectivity than pure Pt and Pd.Thus,to explore their stable structures is crucial for understanding the catalytic performance of nanoparticles.In this paper,the improved particle swarm optimization（PSO）algorithm and the tight binding second-moment approximation potentials are employed to investigate the stable structures of truncated octahedron Pt-Pd alloyed nanoparticles with an atomic amount of 711,1 289,2 075 and composition of 1∶1.Results show that the performance of PSO algorithm has been improved than in the past,and the lower-energy structure has been searched.Aside the improvement,the PSO algorithm overcomes the drawback of premature convergence,and the stability of the algorithm is also improved.