Analyzing ZnO clusters through the density-functional theory
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The potential energy surface of ZnnOn clusters (n = 2, 4, 6, 8) has been explored by using a simulated annealing method. For n = 2, 4, and 6, the CCSD(T)/TZP method was used as the reference, and from here it is shown that the M06-2X/TZP method gives the lowest deviations over PBE, PBE0, B3LYP, M06, and MP2 methods. Thus, with the M06-2X method we predict isomers of ZnnOn clusters, which coincide with some isomers reported previously. By using the atoms in molecules analysis, possible contacts between Zn and O atoms were found for all structures studied in this article. The bond paths involved in several clusters suggest that ZnnOn clusters can be obtained from the zincite (ZnO crystal), such an observation was confirmed for clusters with n = 2 − 9,18 and 20. The structure with n = 23 was obtained by the procedure presented here, from crystal information, which could be important to confirm experimental data delivered for n = 18 and 23.
KeywordsZnO clusters DFT Exchange-correlation functionals Simulated annealing
This article is dedicated to Professor Pratim Kumar Chattaraj for his contributions around density-functional-theory and as part of the celebration of his 60th anniversary. We thank the Laboratorio de Supercómputo y Visualización en Paralelo at the Universidad Autónoma Metropolitana-Iztapalapa for access to their computer facilities. L.-A. S.-A. and R. H.-E. thank CONACYT, México, for the scholarships 265471 and 283251, respectively.
- 22.Abdolhosseini-Sarsari I, Javad-Hashemifar S, Salamati H (2012) First-principles study of ring to cage structural crossover in small ZnO clusters. J Phys: Condens Matter 24:505502Google Scholar
- 35.(2012) Stewart Computational Chemistry, Colorado Springs, CO, USA Mopac. https://OpenMOPAC.net
- 50.Hernández-Esparza R, Mejia-Chica S, Zapata-Escobar A, Guevara-García A, Martínez-Melchor A, Hernández-Pérez J, Vargas R, Garza J (2014) Grid-based algorithm to search critical points, in the electron density, accelerated by graphics processing units. J Comput Chem 35:2272–2278CrossRefPubMedGoogle Scholar
- 51.Hernández-Esparza R, Vázquez-Mayagoitia A, Soriano-Agueda L-A, Vargas R, Garza J (2018) GPUs as boosters to analyze scalar and vector fields in quantum chemistry. Int J Quantum Chem. https://doi.org/10.1002/qua.25671
- 52.Kihara K, Donnay G (1985) Anharmonic thermal vibrations on ZnO. Can Mineral 23:647–654Google Scholar
- 55.Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingGoogle Scholar