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

Jun Zhang; Vassiliki Alexandra Glezakou; Roger Rousseau; Manh Thuong Nguyen

doi:10.1021/acs.jctc.9b01107

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

Jun Zhang, Vassiliki Alexandra Glezakou, Roger Rousseau, Manh Thuong Nguyen

Research output: Contribution to journal › Article › peer-review

55 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	3947-3958
Number of pages	12
Journal	Journal of Chemical Theory and Computation
Volume	16
Issue number	6
DOIs	https://doi.org/10.1021/acs.jctc.9b01107
State	Published - Jun 9 2020
Externally published	Yes

Funding

The work described in this publication was performed at Pacific Northwest National Laboratory (PNNL), which is operated by Battelle for the United States Department of Energy (DOE) under Contract DE-AC05-76RL0180. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. Computer resources were provided by Research Computing at Pacific Northwest National Laboratory (PNNL), and the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

Funders	Funder number
U.S. Department of Energy	DE-AC02-05CH11231, DE-AC05-76RL0180
Office of Science
Basic Energy Sciences
Chemical Sciences, Geosciences, and Biosciences Division
National Energy Research Scientific Computing Center

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10.1021/acs.jctc.9b01107

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title = "NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems",

abstract = "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.",

author = "Jun Zhang and Glezakou, {Vassiliki Alexandra} and Roger Rousseau and Nguyen, {Manh Thuong}",

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AU - Zhang, Jun

AU - Glezakou, Vassiliki Alexandra

AU - Rousseau, Roger

AU - Nguyen, Manh Thuong

PY - 2020/6/9

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N2 - 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.

AB - 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.

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

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