发表机构
Facultad de Ciencias, CUICBAS, Universidad de Colima; Yusuf Hamied Department of Chemistry, University of Cambridge（科利马大学基础科学学院; 剑桥大学尤素福·哈米德化学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种利用对称种子构型和尺寸间信息传播的高效算法，在数千粒子系统中以适中成本寻找低能极小值，并在汤姆孙问题上显著改进大尺寸系统的解，其缺陷模式有助于球形拓扑结构预测。
AI 中文摘要
我们提出一种算法，用于在包含数千个粒子的系统中以适中的计算成本识别低能极小值。我们聚焦于球形晶体，其中粒子在球体表面形成有序结构。我们的方法利用对称种子构型或已知结构，在连续的系统尺寸范围内初始化搜索。新识别出的极小值随后用于指导邻近尺寸下的搜索，使信息能够在构型空间中高效传播。我们已在汤姆孙问题上测试了该方法，汤姆孙问题是少数几个已针对此类尺寸系统进行全局优化的相互作用粒子系统之一。我们显著改进了许多先前的解，特别是在系统尺寸较大时，此时系统的全局优化在计算上代价高昂。由于汤姆孙问题的有利堆积反映在从原子尺度到介观尺度的系统中，新表征的缺陷模式可能为具有球形拓扑的广泛问题提供结构预测信息。
英文摘要
We propose an algorithm for identifying low-energy minima in systems containing thousands of particles, at moderate computational cost. We focus on spherical crystals, in which particles form ordered structures on the surface of a sphere. Our method uses symmetric seed configurations, or known structures, to initialize searches across contiguous ranges of system sizes. Newly identified minima are then used to guide searches at neighbouring sizes, allowing information to propagate efficiently through configuration space. We have tested this approach on the Thomson problem, one of the few interacting-particle systems for which global optimization has been considered for systems of this size. We substantially improve on many previous solutions, particularly at larger system sizes, where systematic global optimization is computationally expensive. Since favourable packings for the Thomson problem are reflected in systems spanning atomistic to mesoscopic length scales, the newly characterized defect patterns may inform structure prediction for a broad range of problems with spherical topology.
Comments29 pages; 11 figures;
