4.7 Article

Prediction of organic molecular crystal geometries from MP2-level fragment quantum mechanical/molecular mechanical calculations

期刊

JOURNAL OF CHEMICAL PHYSICS
卷 137, 期 17, 页码 -

出版社

AMER INST PHYSICS
DOI: 10.1063/1.4764063

关键词

crystal symmetry; density functional theory; electronic structure; lattice constants; molecular electronic states; molecular force constants; organic compounds; wave functions

资金

  1. National Science Foundation [CHE-1112568]
  2. Teragrid [TG-CHE110064]
  3. Direct For Mathematical & Physical Scien
  4. Division Of Chemistry [1112568] Funding Source: National Science Foundation

向作者/读者索取更多资源

The fragment-based hybrid many-body interaction (HMBI) model provides a computationally affordable means of applying electronic structure wavefunction methods to molecular crystals. It combines a quantum mechanical treatment of individual molecules in the unit cell and their short-range pairwise interactions with a polarizable molecular mechanics force-field treatment of long-range and many-body interactions. Here, we report the implementation of analytic nuclear gradients for the periodic model to enable full relaxation of both the atomic positions and crystal lattice parameters. Using a set of five, chemically diverse molecular crystals, we compare the quality of the HMBI MP2/aug-cc-pVDZ-level structures with those obtained from dispersion-corrected periodic density functional theory, B3LYP-D*, and from the Amoeba polarizable force field. The MP2-level structures largely agree with the experimental lattice parameters to within 2%, and the root-mean-square deviations in the atomic coordinates are less than 0.2 angstrom. These MP2 structures are almost as good as those predicted from periodic B3LYP-D*/TZP and are significantly better than those obtained with B3LYP-D*/6-31G(d,p) or with the Amoeba force field. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4764063]

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据