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

• Published in: Journal of chemical theory and computation Vol. 16; no. 6; pp. 3947 - 3958
• Main Authors: Zhang, Jun, Glezakou, Vassiliki-Alexandra, Rousseau, Roger, Nguyen, Manh-Thuong
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 09.06.2020
• Subjects: Adaptive algorithms; Adaptive learning; Adaptive search techniques; Adaptive systems; Algorithms; Boundary conditions; Buckminsterfullerene; Clusters; Fullerenes; Global optimization; Graphene; Machine learning; Nonlinear programming; Optimization; Potential energy; Search engines; Search process; Swarm intelligence; Vapor phases
• ISSN: 1549-9618; 1549-9626; 1549-9626
• DOI: 10.1021/acs.jctc.9b01107

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 Au , ligated Au , Au supported on graphene oxide and defected rutile, and a large cluster assembly [Co Te (PEt ) ][C ] , 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.