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Modelling molecule-surface interactions-an automated quantum-classical approach using a genetic algorithm

机译:建模分子-表面相互作用-使用遗传算法的自动量子经典方法

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摘要

We present an automated and efficient method to develop force fields for molecule-surface interactions. A genetic algorithm (GA) is used to parameterise a classical force field so that the classical adsorption energy landscape of a molecule on a surface matches the corresponding landscape from density functional theory (DFT) calculations. The procedure performs a sophisticated search in the parameter phase space and converges very quickly. The method is capable of fitting a significant number of structures and corresponding adsorption energies. Water on a ZnO(0001) surface was chosen as a benchmark system but the method is implemented in a flexible way and can be applied to any system of interest. In the present case, pairwise Lennard Jones (LJ) and Coulomb potentials are used to describe the molecule-surface interactions. In the course of the fitting procedure, the LJ parameters are refined in order to reproduce the adsorption energy landscape. The classical model is capable of describing a wide range of energies, which is essential for a realistic description of a fluid-solid interface.
机译:我们提出了一种自动有效的方法来开发分子表面相互作用的力场。遗传算法(GA)用于对经典力场进行参数化,以使表面上分子的经典吸附能态与密度泛函理论(DFT)计算得出的相应态相匹配。该过程在参数相空间中执行复杂的搜索,并且收敛很快。该方法能够拟合大量的结构和相应的吸附能。 ZnO(0001)表面的水被选作基准系统,但该方法以灵活的方式实现,可应用于任何感兴趣的系统。在当前情况下,成对的伦纳德·琼斯(LJ)和库仑势用于描述分子与表面的相互作用。在拟合过程中,会优化LJ参数,以重现吸附能图。经典模型能够描述广泛的能量,这对于现实地描述流固界面至关重要。

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