论文标题
一种灵活的自适应网格算法，用于全球优化，利用盆地跳蒙特卡洛
A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo
论文作者
论文摘要
全球优化是原子模拟研究的一个积极研究领域，迄今为止，已经提出了许多算法。一个突出的例子是盆地跳蒙特卡洛，它执行了修改的大都市蒙特卡洛搜索，以探索感兴趣系统的势能表面。由于高维配置搜索空间，这些模拟可能非常苛刻。可以通过利用网格作为原子位置来缩小有效的搜索空间，但如果使用固定的网格，则可能以可能偏向结果。在本文中，我们提出了一种灵活的网格算法，用于全局优化，该算法允许利用网格的效率而不会偏向模拟结果。该方法是一般的，适用于非常异构的系统，例如两种不同晶体结构的材料之间的接口或在表面支撑的大簇之间。作为基准情况，我们证明了其在含有多达100个颗粒的Lennard-Jones簇的全球优化问题中的性能。尽管该模型潜力的简单性，但Lennard-Jones簇还是一个具有挑战性的测试案例，因为一些“魔术”粒子数量的全球最小值表现出与仅略有不同大小的群集的几何形状。
Global optimization is an active area of research in atomistic simulations, and many algorithms have been proposed to date. A prominent example is basin hopping Monte Carlo, which performs a modified Metropolis Monte Carlo search to explore the potential energy surface of the system of interest. These simulations can be very demanding due to the high-dimensional configurational search space. The effective search space can be reduced by utilizing grids for the atomic positions, but at the cost of possibly biasing the results if fixed grids are employed. In this paper, we present a flexible grid algorithm for global optimization that allows to exploit the efficiency of grids without biasing the simulation outcome. The method is general and applicable to very heterogeneous systems, such as interfaces between two materials of different crystal structure or large clusters supported at surfaces. As a benchmark case, we demonstrate its performance for the well-known global optimization problem of Lennard-Jones clusters containing up to 100 particles. In spite of the simplicity of this model potential, Lennard-Jones clusters represent a challenging test case, since the global minima for some "magic" numbers of particles exhibit geometries that are very different from those of clusters with only a slightly different size.
