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A dynamic lattice searching method for fast optimization of Lennard–Jones clusters
Author(s) -
Shao Xueguang,
Cheng Longjiu,
Cai Wensheng
Publication year - 2004
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20096
Subject(s) - lattice (music) , energy minimization , minification , atom (system on chip) , computer science , statistical physics , algorithm , physics , mathematical optimization , mathematics , quantum mechanics , acoustics , embedded system
A highly efficient unbiased global optimization method called dynamic lattice searching (DLS) was proposed. The method starts with a randomly generated local minimum, and finds better solution by a circulation of construction and searching of the dynamic lattice (DL) until the better solution approaches the best solution. The DL is constructed adaptively based on the starting local minimum by searching the possible location sites for an added atom, and the DL searching is implemented by iteratively moving the atom located at the occupied lattice site with the highest energy to the vacant lattice site with the lowest energy. Because the DL can greatly reduce the searching space and the number of the time‐consuming local minimization procedures, the proposed DLS method runs at a very high efficiency, especially for the clusters of larger size. The performance of the DLS is investigated in the optimization of Lennard–Jones (LJ) clusters up to 309 atoms, and the structure of the LJ 500 is also predicted. Furthermore, the idea of dynamic lattice can be easily adopted in the optimization of other molecular or atomic clusters. It may be a promising approach to be universally used for structural optimizations in the chemistry field. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1693–1698, 2004