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分子-表面相互作用的建模——一种使用遗传算法的自动化量子-经典方法。

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

机构信息

Center of Smart Interfaces-TU Darmstadt, Petersenstr. 32, 64287 Darmstadt, Germany.

出版信息

Phys Chem Chem Phys. 2011 Jun 14;13(22):10577-83. doi: 10.1039/c0cp02889d. Epub 2011 May 18.

Abstract

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.

摘要

我们提出了一种自动化且高效的方法,用于开发分子-表面相互作用的力场。遗传算法(GA)用于参数化经典力场,以使分子在表面上的经典吸附能景观与密度泛函理论(DFT)计算的相应景观相匹配。该过程在参数相空间中进行复杂的搜索,并迅速收敛。该方法能够拟合大量的结构和相应的吸附能。选择 ZnO(0001)表面上的水作为基准系统,但该方法以灵活的方式实现,可以应用于任何感兴趣的系统。在当前情况下,使用成对 Lennard-Jones (LJ) 和库仑势来描述分子-表面相互作用。在拟合过程中,LJ 参数被细化以重现吸附能景观。经典模型能够描述广泛的能量范围,这对于真实描述流体-固体界面至关重要。

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