An efficient genetic algorithm for structure prediction at the nanoscale.
An efficient genetic algorithm for structure prediction at the nanoscale.
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DOI：
10.1039/c6nr09072a
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发表时间：
2017-03
期刊：
影响因子：
6.7
通讯作者：
T. Lazauskas;A. Sokol;S. Woodley
中科院分区：
文献类型：
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作者：
T. Lazauskas;A. Sokol;S. Woodley
我们已经开发和实施了一种新的全局优化技术的基础上，拉马克遗传算法的重点是结构多样性。在一个给定的复杂的能源景观的有效搜索的关键过程被证明是使用候选结构的拓扑分析实现的重复的去除。对新形成的结构进行仔细的几何预筛选和引入新的突变移动类进一步提高了成功率。在知识主导的主代码或KLMC中实现的所开发技术的力量通过其定位和探索Lennard-Jones 38原子系统（LJ 38）的具有挑战性的双漏斗景观的能力来证明。我们应用重新开发的KLMC调查三个化学性质不同的系统：离子半导体（ZnO）1-32，金属Ni 13和共价键C60。所有这四个系统已系统地探讨了能源景观定义使用原子间的潜力。新的进展使我们能够成功地定位LJ 38的双漏斗，找到新的局部和全局最小的ZnO团簇，广泛探索Ni 13和C60（巴克敏斯特富勒烯，或巴基球）的势能面。
We have developed and implemented a new global optimization technique based on a Lamarckian genetic algorithm with the focus on structure diversity. The key process in the efficient search on a given complex energy landscape proves to be the removal of duplicates that is achieved using a topological analysis of candidate structures. The careful geometrical prescreening of newly formed structures and the introduction of new mutation move classes improve the rate of success further. The power of the developed technique, implemented in the Knowledge Led Master Code, or KLMC, is demonstrated by its ability to locate and explore a challenging double funnel landscape of a Lennard-Jones 38 atom system (LJ38). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO)1-32, metallic Ni13 and covalently bonded C60. All four systems have been systematically explored on the energy landscape defined using interatomic potentials. The new developments allowed us to successfully locate the double funnels of LJ38, find new local and global minima for ZnO clusters, extensively explore the Ni13 and C60 (the buckminsterfullerene, or buckyball) potential energy surfaces.