TY - JOUR
T1 - NWPEsSe
T2 - An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems
AU - Zhang, Jun
AU - Glezakou, Vassiliki Alexandra
AU - Rousseau, Roger
AU - Nguyen, Manh Thuong
N1 - Funding Information:
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.
Publisher Copyright:
Copyright © 2020 American Chemical Society.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/6/9
Y1 - 2020/6/9
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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U2 - 10.1021/acs.jctc.9b01107
DO - 10.1021/acs.jctc.9b01107
M3 - Article
C2 - 32364725
AN - SCOPUS:85086066170
VL - 16
SP - 3947
EP - 3958
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
SN - 1549-9618
IS - 6
ER -