Efficient and scalable global structure optimization

分子团簇不仅是连接单分子和大块物质的纽带，而且在纳米技术、多相催化和气候研究等许多领域都有着直接的联系。它们的结构和性质不同于单分子和大块物质，通常也不同于化学经验所预期的。因此，具有实际应用价值的聚类结构预测是非常重要的。然而，这是很难实现的，因为集群结构的搜索空间随集群大小呈指数增长。因此，标准的局部优化工具是严重不足的，而且到目前为止建立良好的非确定性全局优化策略（NDGO;例如，进化算法（EA））对于较大的集群变得难以处理。然而，这正是集群实验需要理论支持的地方。因此，本项目的目的是提高全球集群结构的搜索效率，并在改进理论和实验之间的连接。这个项目的延续大部分致力于EA方法的发展。对于局部优化，存在通用且高效的算法。然而，“没有免费的午餐”定理已经证明，这种组合是不可能的NDPUNK。在那里，必须通过利用特定于应用程序的功能来实现高效率。遵循这一核心思想，大多数开发线通过使EA适应手头的问题来提高效率。然而，这需要的非平凡的EA控制并不是强加给用户的;相反，EA是用来检测和“学习”必要的更改的。因此，可以预期，由此产生的自适应EA将不仅是更有效的，而且更容易handle.Another EA方法的开发和两个EA应用程序的一部分集中在一个领域，是新的EA，即聚集的中等大小的有机分子的表面上。在项目的第一阶段，我们已经可以证明环境影响评估在这方面是适用和有用的。然而，在上述意义上（通过特定问题的特征提高效率），需要进一步的方法开发。这两项应用将与正在进行的关于“智能表面”和多相催化的实验项目密切合作。

Molecular clusters are not only the link between single molecules and bulk matter but also have direct relevance in many areas, including nanotechnology, heterogeneous catalysis, and climate research. Their structures and properties differ from that of single molecules and bulk matter, and often also from what is expected by chemical experience. Therefore, practically applicable cluster structure prediction is of prime importance. However, this is hard to achieve since the search space of cluster structures grows exponentially with cluster size. Hence, standard local optimization tools are grossly insufficient, but also the by now well established non-deterministic global optimization strategies (NDGO; e.g., evolutionary algorithms (EA)) become unwieldy for larger clusters. Nevertheless, this is exactly where cluster experiments need theoretical support. Therefore, the present project aims at increases in global cluster structure search efficiency and at improved connections between theory and experiment.Large parts of this project continuation are devoted to EA method development. For local optimization, algorithms exist that are both general and highly efficient. However, the "no free lunch" theorem has proven that this combination is impossible for NDGOs. There, high efficiency has to be achieved by exploiting application-specific features. Following this core idea, most development lines aim at higher efficiency by adapting the EA to the problem at hand. However, the non-trivial EA control that this entails is not forced upon the user; instead, the EA is instrumented to detect and to "learn" the necessary changes. Hence, it can be expected that the resulting adaptive EA will not only be more efficient but also easier to handle.Another part of EA method development and two EA applications focus on an area that is new for EAs, namely aggregation of medium-size organic molecules on surfaces. In the first project phase, we could already demonstrate that EAs are applicable and useful in this area. In the above sense (efficiency via problem-specific features), however, further method development is needed. The two applications will be conducted in close collaboration with ongoing experimental projects, on "smart surfaces" and on heterogeneous catalysis.