MOLECULAR-GEOMETRY OPTIMIZATION WITH A GENETIC ALGORITHM
MOLECULAR-GEOMETRY OPTIMIZATION WITH A GENETIC ALGORITHM
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DOI：
10.1103/physrevlett.75.288
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发表时间：
1995-07-10
影响因子：
8.6
通讯作者：
HO, KM
中科院分区：
文献类型：
--
作者：
DEAVEN, DM;HO, KM
本文提出了一种在任意模型势下可靠地确定原子团簇最低能量结构的方法。该方法是基于遗传算法，它操作的候选结构的人口，以产生新的候选人具有较低的能量。我们的方法显着优于模拟退火，我们证明了通过应用遗传算法的碳紧结合模型的潜力。有了这个潜力，该算法有效地发现富勒烯簇结构C 60开始从随机原子坐标。
We present a method for reliably determining the lowest energy structure of an atomic cluster in an arbitrary model potential. The method is based on a genetic algorithm, which operates on a population of candidate structures to produce new candidates with lower energies. Our method dramatically outperforms simulated annealing, which we demonstrate by applying the genetic algorithm to a tight-binding model potential for carbon. With this potential, the algorithm efficiently finds fullerene cluster structures up to C 60 starting from random atomic coordinates.