NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

Global optimization constitutes an
important and fundamental problem
in theoretical studies in many chemical fields, such as catalysis,
materials, or separations problems. In this paper, a novel algorithm has been developed
for the global optimization of large systems including neat and ligated
clusters in the gas phase and supported clusters in periodic boundary
conditions. The method is based on an updated artificial bee colony
(ABC) algorithm method, that allows for adaptive-learning during the
search process. The new algorithm is tested against four classes of
systems of diverse chemical nature: gas phase Au55, ligated
Au82+, Au8 supported on graphene oxide and defected rutile, and a large
cluster assembly [Co6Te8(PEt3)6]­[C60]n, with sizes
ranging between 1 and 3 nm and containing up to 1300 atoms. Reliable
global minima (GMs) are obtained for all cases, either confirming
published data or reporting new lower energy structures. The algorithm
and interface to other codes in the form of an independent program,
Northwest Potential Energy Search Engine (NWPEsSe), is freely available,
and it provides a powerful and efficient approach for global optimization
of nanosized cluster systems.

全局优化在众多化学领域的理论研究中占据着至关重要的基础地位，例如催化、材料或分离问题。本文提出了一种针对气相中纯簇和配位簇以及周期性边界条件下的支撑簇等大型系统全局优化问题的新型算法。该方法基于更新的人工蜂群（ABC）算法，能够在搜索过程中实现自适应学习。该新算法针对四种具有不同化学性质的系统进行了测试：气相Au55、配位Au82+、石墨烯氧化物和缺陷二氧化钛上的Au8，以及大型簇组装体[Co6Te8(PEt3)6]­[C60]n，其尺寸介于1至3纳米之间，包含多达1300个原子。对于所有情况，均获得了可靠的全球最小值（GMs），要么证实了已发表的数据，要么报告了新的更低能量结构。该算法及与其他代码接口的独立程序——西北势能搜索引擎（NWPEsSe）——免费提供，为纳米尺寸簇系统的全局优化提供了一种强大而高效的方法。