Efficient and scalable global structure optimization

高效且可扩展的全局结构优化

• 批准号: 280675984
• 负责人: Professor Dr. Bernd Hartke
• 金额: --
• 依托单位: Institut für Physikalische Chemie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/280675984?language=en
• 关键词: Efficient; scalable; global; structure; optimization

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.

分子簇不仅是单分子与散装物质之间的联系，而且在许多领域都具有直接相关性，包括纳米技术，异质催化和气候研究。它们的结构和特性与单分子和散装物质不同，通常与化学体验所期望的不同。因此，实际上适用的集群结构预测至关重要。但是，这很难实现，因为群集结构的搜索空间随着群集大小而成倍增长。因此，标准的本地优化工具严重不足，但目前既建立的非确定性全球优化策略（NDGO;例如，进化算法（EA））变得笨拙。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群集结构搜索效率，并提高理论与实验之间的联系。该项目延续的一部分专门用于EA方法开发。对于局部优化，存在既通用又高效的算法。但是，“无免费午餐”定理证明了这种组合对于NDGO是不可能的。在那里，必须利用特定于应用程序的功能来实现高效率。遵循这个核心想法，大多数开发线路通过将EA调整到手头的问题来旨在提高效率。但是，这种不仅限制的EA控制并不是强迫用户的。取而代之的是，EA被仪器以检测和“学习”必要的更改。因此，可以预期，由此产生的自适应EA不仅会更有效，而且更容易处理。AEA方法开发的另一部分和两个EA应用集中在一个新的EAS区域，即在表面上的中型有机分子聚集。在第一个项目阶段，我们已经可以证明EA在这一领域适用且有用。在上述意义上（通过特定于问题的特征效率），需要进一步的方法开发。这两个应用程序将与正在进行的实验项目，“智能表面”和异质催化进行密切合作。