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Searching for low-energy structures of nanoparticles: a comparison of different methods and algorithms

机译:寻找纳米粒子的低能结构:不同方法和算法的比较

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Nanoparticles can have unusual, low symmetry or non-crystalline shapes. Since structure determines nanoparticle physical and chemical properties, many efforts have been devoted to predict what are the most stable structural motifs depending on cluster size and composition. The global optimization of the 3N-dimensional potential energy surface of a nanocluster is nevertheless a very difficult computational problem. Here we depict the scenery of the global optimization strategies applied to the study of nanoclusters, focusing on genetic and Basin-hopping approaches. Moreover, several strategies to improve Basin-hopping efficiency are discussed and compared through the optimization of test-systems with different size and composition.
机译:纳米颗粒可以具有不寻常的,低对称性或非晶态的形状。由于结构决定了纳米粒子的物理和化学性质,因此根据簇的大小和组成,人们做出了许多努力来预测什么是最稳定的结构基序。但是,纳米团簇的3N维势能面的全局优化是一个非常困难的计算问题。在这里,我们描述了应用于纳米团簇研究的全局优化策略,重点介绍了遗传和流域跳槽方法。此外,通过优化具有不同大小和组成的测试系统,讨论并比较了几种提高流域跳水效率的策略。

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