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

preprint
revised on 20.04.2020 and posted on 21.04.2020 by Jun Zhang, Vassiliki-Alexandra Glezakou, Roger Rousseau, Manh-Thuong Nguyen
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 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 to 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.

History

Email Address of Submitting Author

zhangjunqcc@gmail.com

Institution

Pacific Northwest National Laboratory

Country

United States

ORCID For Submitting Author

0000-0001-9093-9430

Declaration of Conflict of Interest

The authors declare no competing financial interests.

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in Journal of Chemical Theory and Computation

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