Narayanan Badri, Redfern Paul C, Assary Rajeev S, Curtiss Larry A
Department of Mechanical Engineering , University of Louisville , Louisville , Kentucky 40292 , USA.
Materials Science Division , Argonne National Laboratory , Argonne , Illinois 60439 , USA . Email:
Chem Sci. 2019 Jun 27;10(31):7449-7455. doi: 10.1039/c9sc02834j. eCollection 2019 Aug 21.
The energies of the 133 000 molecules in the GDB-9 database have been calculated at the G4MP2 level of theory and then were used to calculate their enthalpies of formation. This database contains organic molecules having nine or less atoms of carbon, nitrogen, oxygen, and fluorine, as well as hydrogen atoms. The accuracy of the G4MP2 energies was investigated on a subset of 459 of the molecules having experimental enthalpies of formation with small uncertainties. On this subset the G4MP2 enthalpies of formation have an accuracy of 0.79 kcal mol, which is similar to its accuracy previously reported for the smaller G3/05 test set. An error analysis of the theoretical enthalpies of formation of the 459 molecules is presented in terms of the size and type of the molecules. Three different density functionals (B3LYP, ωB97X-D, M06-2X) were also assessed on 459 molecules of accurate enthalpy data for comparison with the G4MP2 results. The G4MP2 energies for the 133 K molecules provide a database that can be used to calculate accurate reaction energies as well as to assess new or existing experimental enthalpies of formation. Several examples are given of types of reactions that can be predicted using the G4MP2 database of energies. The G4MP2 energies of the GDB-9 molecules will also be useful in future investigations of applications of machine learning to quantum chemical data.
GDB - 9数据库中133000个分子的能量已在G4MP2理论水平下进行了计算,然后用于计算它们的生成焓。该数据库包含碳、氮、氧、氟原子数为九个或更少以及氢原子的有机分子。在具有小不确定性的实验生成焓的459个分子的子集上研究了G4MP2能量的准确性。在这个子集上,G4MP2生成焓的准确性为0.79千卡/摩尔,这与其先前针对较小的G3/05测试集报告的准确性相似。根据分子的大小和类型对459个分子的理论生成焓进行了误差分析。还对459个具有准确焓数据的分子评估了三种不同的密度泛函(B3LYP、ωB97X - D、M06 - 2X),以便与G4MP2结果进行比较。133000个分子的G4MP2能量提供了一个数据库,可用于计算准确的反应能量以及评估新的或现有的实验生成焓。给出了几个可以使用G4MP2能量数据库预测的反应类型的示例。GDB - 9分子的G4MP2能量在未来机器学习应用于量子化学数据的研究中也将是有用的。