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纳米团簇的结构预测;在密度泛函理论(DFT)能量景观上进行直接搜索还是预筛选搜索?

Structure prediction of nanoclusters; a direct or a pre-screened search on the DFT energy landscape?

作者信息

Farrow M R, Chow Y, Woodley S M

机构信息

Department of Chemistry, Kathleen Lonsdale Materials Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK.

出版信息

Phys Chem Chem Phys. 2014 Oct 21;16(39):21119-34. doi: 10.1039/c4cp01825g. Epub 2014 Jul 14.

DOI:10.1039/c4cp01825g
PMID:25017305
Abstract

The atomic structure of inorganic nanoclusters obtained via a search for low lying minima on energy landscapes, or hypersurfaces, is reported for inorganic binary compounds: zinc oxide (ZnO)n, magnesium oxide (MgO)n, cadmium selenide (CdSe)n, and potassium fluoride (KF)n, where n = 1-12 formula units. The computational cost of each search is dominated by the effort to evaluate each sample point on the energy landscape and the number of required sample points. The effect of changing the balance between these two factors on the success of the search is investigated. The choice of sample points will also affect the number of required data points and therefore the efficiency of the search. Monte Carlo based global optimisation routines (evolutionary and stochastic quenching algorithms) within a new software package, viz. Knowledge Led Master Code (KLMC), are employed to search both directly and after pre-screening on the DFT energy landscape. Pre-screening includes structural relaxation to minimise a cheaper energy function - based on interatomic potentials - and is found to improve significantly the search efficiency, and typically reduces the number of DFT calculations required to locate the local minima by more than an order of magnitude. Although the choice of functional form is important, the approach is robust to small changes to the interatomic potential parameters. The computational cost of initial DFT calculations of each structure is reduced by employing Gaussian smearing to the electronic energy levels. Larger (KF)n nanoclusters are predicted to form cuboid cuts from the rock-salt phase, but also share many structural motifs with (MgO)n for smaller clusters. The transition from 2D rings to 3D (bubble, or fullerene-like) structures occur at a larger cluster size for (ZnO)n and (CdSe)n. Differences between the HOMO and LUMO energies, for all the compounds apart from KF, are in the visible region of the optical spectrum (2-3 eV); KF lies deep in the UV region at 5 eV and shows little variation. Extrapolating the electron affinities found for the clusters with respect to size results in the qualitatively correct work functions for the respective bulk materials.

摘要

本文报道了通过在能量景观或超曲面上寻找低能量极小值而获得的无机纳米团簇的原子结构,涉及无机二元化合物:氧化锌(ZnO)n、氧化镁(MgO)n、硒化镉(CdSe)n和氟化钾(KF)n,其中n = 1 - 12个化学式单元。每次搜索的计算成本主要取决于评估能量景观上每个采样点的工作量以及所需采样点的数量。研究了改变这两个因素之间的平衡对搜索成功的影响。采样点的选择也会影响所需数据点的数量,进而影响搜索效率。在一个新的软件包,即知识引导主代码(KLMC)中,基于蒙特卡罗的全局优化例程(进化算法和随机淬火算法)被用于直接搜索以及在DFT能量景观上进行预筛选后搜索。预筛选包括结构弛豫,以基于原子间势最小化一个更便宜的能量函数,并且发现这能显著提高搜索效率,通常将定位局部极小值所需的DFT计算数量减少一个数量级以上。尽管函数形式的选择很重要,但该方法对原子间势参数的小变化具有鲁棒性。通过对电子能级采用高斯展宽,降低了每个结构初始DFT计算的成本。预测较大的(KF)n纳米团簇会从岩盐相中形成长方体切面,但对于较小的团簇,它们也与(MgO)n共享许多结构基序。对于(ZnO)n和(CdSe)n,从二维环结构到三维(气泡状或富勒烯状)结构的转变发生在更大的团簇尺寸下。除KF外,所有化合物的最高占据分子轨道(HOMO)和最低未占据分子轨道(LUMO)能量之差在光谱的可见光区域(2 - 3 eV);KF位于紫外区域深处,为5 eV,且变化很小。根据团簇尺寸外推得到的电子亲和势,得到了相应体材料定性正确的功函数。

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