1. Kick–Fukui: A Fukui Function-Guided Method for Molecular Structure Prediction
- Author
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William Tiznado, Diego Inostroza, Julia Contreras-García, Walter A. Rabanal-León, Osvaldo Yañez, Rodrigo Báez-Grez, Ricardo Pino-Rios, Carlos Cárdenas, Universidad de Chile = University of Chile [Santiago] (UCHILE), Universidad Andrés Bello [Santiago] (UNAB), Universidad de Santiago de Chile [Santiago] (USACH), Universidad de Concepción [Chile], Laboratoire de chimie théorique (LCT), and Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Physics ,education.field_of_study ,Molecular Structure ,010304 chemical physics ,General Chemical Engineering ,Population ,Evolutionary algorithm ,General Chemistry ,Library and Information Sciences ,010402 general chemistry ,01 natural sciences ,Small set ,0104 chemical sciences ,Computer Science Applications ,[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry ,Maxima and minima ,0103 physical sciences ,Potential energy surface ,Humans ,Density functional theory ,Statistical physics ,education ,Gradient method ,Algorithms ,Fukui function - Abstract
Here, we introduce a hybrid method, named Kick-Fukui, to explore the potential energy surface (PES) of clusters and molecules using the Coulombic integral between the Fukui functions in the first screening of the best individuals. In the process, small stable molecules or clusters whose combination has the stoichiometry of the explored species are used as assembly units. First, a small set of candidates has been selected from a large and stochastically generated (Kick) population according to the maximum value of the Coulombic integral between the Fukui functions of both fragments. Subsequently, these few candidates are optimized using a gradient method and density functional theory (DFT) calculations. The performance of the program has been evaluated to explore the PES of various systems, including atomic and molecular clusters. In most cases studied, the global minimum (GM) has been identified with a low computational cost. The strategy does not allow to identify the GM of some silicon clusters; however, it predicts local minima very close in energy to the GM that could be used as the initial population of evolutionary algorithms.
- Published
- 2021