Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters
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ORIGINAL PAPER
Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters ˜ 1 Breno R. L. Galvao
· Lu´ıs P. Viegas2 · Dennis R. Salahub3 · Maicon P. Lourenc¸o4
Received: 1 May 2020 / Accepted: 21 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this work, we explore the possibility of using computationally inexpensive electronic structure methods, such as semiempirical and DFTB calculations, for the search of the global minimum (GM) structure of chemical systems. The basic prerequisite that these inexpensive methods will need to fulfill is that their lowest energy structures can be used as starting point for a subsequent local optimization at a benchmark level that will yield its GM. If this is possible, one could bypass the global optimization at the expensive method, which is currently impossible except for very small molecules. Specifically, we test our methods with clusters of second row elements including systems of several bonding types, such as alkali, metal, and covalent clusters. The results reveal that the DFTB3 method yields reasonable results and is a potential candidate for this type of applications. Even though the DFTB2 approach using standard parameters is proven to yield poor results, we show that a re-parametrization of only its repulsive part is enough to achieve excellent results, even when applied to larger systems outside the training set. Keywords Global optimization · Semiempirical · DFTB
Introduction The determination of the lowest energy structure of a chemical system is a very difficult optimization problem due to the large number of local minima existing in its potential energy surface (PES), which is known to increase exponentially with the number of atoms in the molecule [1]. This article belongs to the Topical Collection XX-Brazilian Symposium of Theoretical Chemistry (SBQT2019) Breno R. L. Galv˜ao
[email protected] 1
Centro Federal de Educac¸a˜ o Tecnol´ogica de Minas Gerais, CEFET-MG, Av. Amazonas 5253, Belo Horizonte, Minas Gerais, 30421-169, Brazil
2
Coimbra Chemistry Center and Chemistry Department, University of Coimbra, 3004-535, Coimbra, Portugal
3
Department of Chemistry, CMS — Centre for Molecular Simulation, IQST — Institute for Quantum Science and Technology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
4
Departamento de Qu´ımica e F´ısica, Centro de Ciˆencias Exatas, Naturais e da Sa´ude (CCENS), Universidade Federal do Esp´ırito Santo, Alegre, Esp´ırito Santo, 29500-000, Brazil
Global optimization (GO) techniques are a class of intelligent algorithms that can explore the PES of a given system to efficiently find its GM. Examples of such GO methods that are widely applied for chemical systems include some that are population based and inspired in nature, such as genetic algorithms [2] (GAs) and particle swarm optimization [3], and also thermodynamically inspired, such as the basin-hopping [4] (BH) method. However, it is not possible to us
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