S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

非共价相互作用 计算机科学 水准点(测量) 离解(化学) 参数化(大气建模) 量子化学 集合(抽象数据类型) 可扩展性 统计物理学 计算化学 化学 物理 分子 数据库 量子力学 物理化学 超分子化学 氢键 大地测量学 辐射传输 程序设计语言 地理
作者
Jan Řezáč,Kevin E. Riley,Pavel Hobza
出处
期刊:Journal of Chemical Theory and Computation [American Chemical Society]
卷期号:7 (8): 2427-2438 被引量:999
标识
DOI:10.1021/ct2002946
摘要

With numerous new quantum chemistry methods being developed in recent years and the promise of even more new methods to be developed in the near future, it is clearly critical that highly accurate, well-balanced, reference data for many different atomic and molecular properties be available for the parametrization and validation of these methods. One area of research that is of particular importance in many areas of chemistry, biology, and material science is the study of noncovalent interactions. Because these interactions are often strongly influenced by correlation effects, it is necessary to use computationally expensive high-order wave function methods to describe them accurately. Here, we present a large new database of interaction energies calculated using an accurate CCSD(T)/CBS scheme. Data are presented for 66 molecular complexes, at their reference equilibrium geometries and at 8 points systematically exploring their dissociation curves; in total, the database contains 594 points: 66 at equilibrium geometries, and 528 in dissociation curves. The data set is designed to cover the most common types of noncovalent interactions in biomolecules, while keeping a balanced representation of dispersion and electrostatic contributions. The data set is therefore well suited for testing and development of methods applicable to bioorganic systems. In addition to the benchmark CCSD(T) results, we also provide decompositions of the interaction energies by means of DFT-SAPT calculations. The data set was used to test several correlated QM methods, including those parametrized specifically for noncovalent interactions. Among these, the SCS-MI-CCSD method outperforms all other tested methods, with a root-mean-square error of 0.08 kcal/mol for the S66 data set.
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