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首页> 外文期刊>New Journal of Chemistry >Relative energies of water nanoclusters (H2O)(20): comparison of empirical and nonempirical double-hybrids with generalized energy-based fragmentation approach
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Relative energies of water nanoclusters (H2O)(20): comparison of empirical and nonempirical double-hybrids with generalized energy-based fragmentation approach

机译:水纳米团簇(H2O)的相对能量(20):使用基于能量的广义碎片化方法比较经验性和非经验性双杂化

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摘要

Double-hybrid (DH) approximations have proved to be powerful tools for computing various properties through the density functional theory (DFT). In the present work, we analyze the performances of some recently proposed parameterized (empirical) and parameter-free (nonempirical) DH density functionals based on Perdew-Burke-Ernzerhof (PBE) and Becke-Lee-Yang-Parr (BLYP) approximations along with their parent methods for predicting the relative energies of 10 low-energy isomers of water nanoclusters (H2O)(20). As a reference to benchmark the performance of functionals, we employ a novel methodology based on generalized energy-based fragmentation (GEBF) approach, in which canonical Hartree-Fock (HF) total energy is combined with the GEBF-CCSD(T) correlation energy, GEBF-CCSD(T)/HF, at the complete basis set limit. It is shown that the PBE-based functionals perform better than the functionals that include BLYP approximation. Of the tested parameterized DHs, B2GP-PLYP performs the best, while among the family of parameter-free models, the PBE-QIDH functional outperforms others. Putting all the results together, the parameter-free adiabatic connection-based functional PBE-QIDH is found to be superior for overall performance. Dissecting the role of both exact exchange and second-order Moller-Plesset perturbation theory (MP2) correlation on the relative energies of water 20-mers, we find that a particular compromise between the two weights is needed to reach a reasonable accuracy. However, it turns out that it is difficult to use the DH approximations to predict the correct relative order of stabilities for the considered water nanoclusters. On the whole, this study points out that if the computational cost is not a serious bottleneck for DH density functional computations on large nanostructures and nanomaterials, older approximations can be superseded by new DHs with the aim of reaching the increased accuracy.
机译:通过密度泛函理论(DFT),双杂化(DH)逼近已被证明是用于计算各种特性的强大工具。在当前的工作中,我们基于Perdew-Burke-Ernzerhof(PBE)和Becke-Lee-Yang-Parr(BLYP)近似分析了一些最近提出的参数化(经验)和无参数(非经验)DH密度函数的性能。用其父级方法预测水纳米簇(H2O)的10种低能异构体的相对能(20)。作为基准,以功能的性能为基准,我们采用了一种基于广义基于能量的碎片化(GEBF)方法的新颖方法,该方法将规范的Hartree-Fock(HF)总能量与GEBF-CCSD(T)相关能量结合在一起,GEBF-CCSD(T)/ HF(以完整基准集为上限)。结果表明,基于PBE的功能比包括BLYP逼近的功能更好。在测试的参数化DH中,B2GP-PLYP的性能最佳,而在无参数模型系列中,PBE-QIDH的性能优于其他参数。将所有结果放在一起,可以发现基于无参数绝热连接的功能性PBE-QIDH对于整体性能而言是优越的。剖析了精确交换和二阶Moller-Plesset微扰理论(MP2)相关性对20-mers水的相对能量的作用,我们发现需要两个权重之间的特殊折衷才能达到合理的精度。但是,事实证明,对于所考虑的水纳米团簇,很难使用DH逼近来预测正确的相对稳定性顺序。总体而言,该研究指出,如果对于大型纳米结构和纳米材料的DH密度函数计算而言,如果计算成本不是严重的瓶颈,则可以用新的DH取代较旧的近似值,以达到提高准确性的目的。

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