A General Database for Main Group Thermochemistry, Kinetics, and Noncovalent Interactions - Assessment of Common and Reparameterized (meta-)GGA Density Functionals
A General Database for Main Group Thermochemistry, Kinetics, and Noncovalent Interactions - Assessment of Common and Reparameterized (meta-)GGA Density Functionals
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DOI：
10.1021/ct900489g
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发表时间：
2010-01-01
影响因子：
5.5
通讯作者：
Grimme, Stefan
中科院分区：
文献类型：
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作者：
Goerigk, Lars;Grimme, Stefan
我们提出了一个量子化学基准数据库的一般主群热化学，动力学和非共价相互作用（GMTKN 24）。这是一个前所未有的汇编24个不同的，化学相关的子集，无论是从现有的数据库或首次在这里提出。完整的一套涉及总共1.049个原子和分子单点计算，并包括731个数据点（相对化学能）的基础上准确的理论或实验参考值。GMTKN 24数据库的有用性是通过应用常见的密度泛函（Meta）广义梯度近似（GGA），混合GGA，双混合GGA水平，包括经验伦敦色散校正。此外，我们改装的功能参数的四个（Meta）GGA泛函的基础上，包含143个系统，包括7个化学上不同的问题的拟合集。对GMTKN 24和分子结构（键长）数据库的验证表明，重新参数化不会改变键长太多，而能量性质的描述更倾向于参数的值。经验色散校正也经常改善传统的热力学问题，并使功能的性能更均匀的整个数据库。对于建议的GMTKN 24集合中的大多数子集，重新拟合的泛函通常具有较低的平均绝对偏差。然而，这也常常伴随着对其他一些重要子集的不良性能的代价。因此，仅仅通过重新参数化现有的函数来创建一个广泛适用的（并且总体上更好的）函数似乎是困难的。然而，这项基准研究。揭示了重新优化的（即，经验）版本的TIPSS-D泛函（oTPSS-D）对于各种问题表现良好，并且可以满足改进的泛函的标准。我们建议对这个新的基准集的编译验证作为一个明确的方式来评估一个新的量子化学方法的真实性能。
We present a quantum chemistry benchmark database for general main group thermochemistry, kinetics, and noncovalent interactions (GMTKN24). It is an unprecedented compilation of 24 different, chemically relevant subsets that either are taken from already existing databases or are presented here for the first time. The complete set involves a total of 1.049 atomic and molecular single point calculations and comprises 731 data points (relative chemical energies) based on accurate theoretical or experimental reference values. The usefulness of the GMTKN24 database is shown by applying common density functionals on the (meta-)generalized gradient approximation (GGA), hybrid-GGA, and double-hybrid-GGA levels to it, including an empirical London dispersion correction. Furthermore, we refitted the functional parameters of four (meta-)GGA functionals based on a fit set containing 143 systems, comprising seven chemically different problems. Validation against the GMTKN24 and the molecular structure (bond lengths) databases shows that the reparameterization does not change bond lengths much, whereas the description of energetic properties is more prone to the parameters' values. The empirical dispersion correction also often improves for conventional thermodynamic problems and makes a functional's performance more uniform over the entire database. The refitted functionals typically have a lower mean absolute deviation for the majority of subsets in the proposed GMTKN24 set. This, however, is also often accompanied at the expense of poor performance for a few other important subsets. Thus, creating a broadly applicable (and overall better) functional by just reparameterizing existing ones seems to be difficult. Nevertheless, this benchmark study. reveals that a reoptimized (i.e., empirical) version of the TIPSS-D functional (oTPSS-D) performs well for a variety of problems and may meet the standards of an improved functional. We propose validation against this new compilation of benchmark sets as a definitive way to evaluate a new quantum chemical method's true performance.