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

• DOI: 10.1021/acs.jctc.9b01107
• Publication Date: 2020-05-04T18:57:56Z
• Authors: Jun Zhang; Vassiliki-Alexand...; Roger Rousseau; Manh-Thuong Nguyen
• Keywords: Maxima and minima; Global Optimization; Energy minimization

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