Jørgensen Mathias S, Larsen Uffe F, Jacobsen Karsten W, Hammer Bjørk
Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University , DK-8000 Aarhus, Denmark.
Center for Atomic-Scale Materials Design (CAMD), Department of Physics, Technical University of Denmark , DK-2800 Kongens Lyngby, Denmark.
J Phys Chem A. 2018 Feb 8;122(5):1504-1509. doi: 10.1021/acs.jpca.8b00160. Epub 2018 Jan 29.
The ability to navigate vast energy landscapes of molecules, clusters, and solids is a necessity for discovering novel compounds in computational chemistry and materials science. For high-dimensional systems, it is only computationally feasible to search a small portion of the landscape, and hence, the search strategy is of critical importance. Introducing Bayesian optimization concepts in an evolutionary algorithm framework, we quantify the concepts of exploration and exploitation in global minimum searches. The method allows us to control the balance between probing unknown regions of the landscape (exploration) and investigating further regions of the landscape known to have low-energy structures (exploitation). The search for global minima structures proves significantly faster with the optimal balance for three test systems (molecular compounds) and to a lesser extent also for a crystalline surface reconstruction. In addition, global search behaviors are analyzed to provide reasonable grounds for an optimal balance for different problems.
在计算化学和材料科学中，探索分子、团簇和固体的巨大能量景观的能力是发现新型化合物的必要条件。对于高维系统，仅在计算上可行的是搜索景观的一小部分，因此，搜索策略至关重要。在进化算法框架中引入贝叶斯优化概念，我们在全局最小搜索中量化了探索和利用的概念。该方法使我们能够控制探索景观未知区域（探索）和进一步研究已知具有低能量结构的景观区域（利用）之间的平衡。对于三个测试系统（分子化合物），以最佳平衡进行全局极小值结构搜索的速度明显更快，对于晶体表面重构，在较小程度上也是如此。此外，还分析了全局搜索行为，为不同问题的最佳平衡提供合理依据。
