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基于神经网络和新颖性采样的金属表面氢解离的从头算分子动力学

Ab initio molecular dynamics of hydrogen dissociation on metal surfaces using neural networks and novelty sampling.

作者信息

Ludwig Jeffery, Vlachos Dionisios G

机构信息

Department of Chemical Engineering and Center for Catalytic Science and Technology, University of Delaware, Newark, Delaware 19716-3110, USA.

出版信息

J Chem Phys. 2007 Oct 21;127(15):154716. doi: 10.1063/1.2794338.

Abstract

We outline a hybrid multiscale approach for the construction of ab initio potential energy surfaces (PESs) useful for performing six-dimensional (6D) classical or quantum mechanical molecular dynamics (MD) simulations of diatomic molecules reacting at single crystal surfaces. The algorithm implements concepts from the corrugation reduction procedure, which reduces energetic variation in the PES, and uses neural networks for interpolation of smoothed ab initio data. A novelty sampling scheme is implemented and used to identify configurations that are most likely to be predicted inaccurately by the neural network. This hybrid multiscale approach, which couples PES construction at the electronic structure level to MD simulations at the atomistic scale, reduces the number of density functional theory (DFT) calculations needed to specify an accurate PES. Due to the iterative nature of the novelty sampling algorithm, it is possible to obtain a quantitative measure of the convergence of the PES with respect to the number of ab initio calculations used to train the neural network. We demonstrate the algorithm by first applying it to two analytic potentials, which model the H2/Pt(111) and H2/Cu(111) systems. These potentials are of the corrugated London-Eyring-Polanyi-Sato form, which are based on DFT calculations, but are not globally accurate. After demonstrating the convergence of the PES using these simple potentials, we use DFT calculations directly and obtain converged semiclassical trajectories for the H2/Pt(111) system at the PW91/generalized gradient approximation level. We obtain a converged PES for a 6D hydrogen-surface dissociation reaction using novelty sampling coupled directly to DFT. These results, in excellent agreement with experiments and previous theoretical work, are compared to previous simulations in order to explore the sensitivity of the PES (and therefore MD) to the choice of exchange and correlation functional. Despite having a lower energetic corrugation in our PES, we obtain a broader reaction probability curve than previous simulations, which is attributed to increased geometric corrugation in the PES and the effect of nonparallel dissociation pathways.

摘要

我们概述了一种混合多尺度方法,用于构建从头算势能面(PESs),该势能面可用于对在单晶表面发生反应的双原子分子进行六维(6D)经典或量子力学分子动力学(MD)模拟。该算法采用了来自波纹减少程序的概念,该程序可减少PES中的能量变化,并使用神经网络对平滑后的从头算数据进行插值。实施了一种新颖的采样方案,并用于识别神经网络最有可能预测不准确的构型。这种混合多尺度方法将电子结构水平的PES构建与原子尺度的MD模拟相结合,减少了指定准确PES所需的密度泛函理论(DFT)计算数量。由于新颖采样算法的迭代性质,可以获得PES相对于用于训练神经网络的从头算计算数量的收敛定量度量。我们首先将该算法应用于两个解析势,它们对H2/Pt(111)和H2/Cu(111)系统进行建模,以此来演示该算法。这些势具有波纹状的伦敦 - 艾林 - 波拉尼 - 佐藤形式,它们基于DFT计算,但并非全局准确。在使用这些简单势演示了PES的收敛性之后,我们直接使用DFT计算,并在PW91/广义梯度近似水平上获得了H2/Pt(111)系统的收敛半经典轨迹。我们使用直接与DFT耦合的新颖采样获得了六维氢 - 表面解离反应的收敛PES。将这些与实验和先前理论工作高度吻合的结果与先前的模拟进行比较,以探索PES(进而MD)对交换和相关泛函选择的敏感性。尽管我们的PES中能量波纹较低,但我们获得的反应概率曲线比先前的模拟更宽,这归因于PES中几何波纹的增加以及非平行解离途径的影响。

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