Global optimization of the structure of atomic nanoparticles is often hampered by the presence of many funnels on the potential energy surface. While broad funnels are readily encountered and easily exploited by the search, narrow funnels are more difficult to locate and explore, presenting a problem if the global minimum is situated in such a funnel. Here, a divide-and-conquer approach is applied to overcome the issue posed by the multifunnel effect using a machine learning approach, without using a priori knowledge of the potential energy surface. This approach begins with a truncated exploration to gather coarse-grained knowledge of the potential energy surface. This is then used to train a machine learning Gaussian mixture model to divide up the potential energy surface into separate regions, with each region then being explored in more detail (or conquered) separately. This scheme was tested on a variety of multifunnel systems and yielded significant improvements to the times taken to locate the global minima of Lennard-Jones (LJ) nanoparticles, LJ75 and LJ104, as well as two metallic systems, Au55 and Pd88. However, difficulties were encountered for LJ98, providing insight into how the scheme could be further improved.

中文翻译：

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一种使用机器学习进行纳米粒子全局优化的分而治之的方法。


原子纳米粒子结构的全局优化通常受到势能表面上存在许多漏斗的阻碍。虽然搜索很容易遇到和利用宽漏斗，但窄漏斗更难定位和探索，如果全局最小值位于此类漏斗中，则会出现问题。在这里，应用了一种分而治之的方法，使用机器学习方法克服了多漏斗效应带来的问题，而无需使用势能表面的先验知识。这种方法从截断探索开始，以收集势能表面的粗粒度知识。然后，使用它来训练机器学习高斯混合模型，将势能表面划分为单独的区域，然后分别对每个区域进行更详细的探索（或征服）。该方案在各种多漏斗系统上进行了测试，并显着缩短了定位 Lennard-Jones （LJ） 纳米颗粒 LJ75 和 LJ104 以及两个金属系统 Au55 和 Pd88 的全局最小值所需的时间。然而，LJ98 遇到了困难，为如何进一步改进该计划提供了见解。