Yu Haoyu S, He Xiao, Li Shaohong L, Truhlar Donald G
Department of Chemistry , Chemical Theory Center , Inorganometallic Catalyst Design Center , Minnesota Supercomputing Institute , University of Minnesota , Minneapolis , Minnesota 55455-0431 , USA . Email:
State Key Laboratory of Precision Spectroscopy and Department of Physics , East China Normal University , Shanghai , 200062 , China.
Chem Sci. 2016 Aug 1;7(8):5032-5051. doi: 10.1039/c6sc00705h. Epub 2016 Apr 6.
Kohn-Sham density functionals are widely used; however, no currently available exchange-correlation functional can predict all chemical properties with chemical accuracy. Here we report a new functional, called MN15, that has broader accuracy than any previously available one. The properties considered in the parameterization include bond energies, atomization energies, ionization potentials, electron affinities, proton affinities, reaction barrier heights, noncovalent interactions, hydrocarbon thermochemistry, isomerization energies, electronic excitation energies, absolute atomic energies, and molecular structures. When compared with 82 other density functionals that have been defined in the literature, MN15 gives the second smallest mean unsigned error (MUE) for 54 data on inherently multiconfigurational systems, the smallest MUE for 313 single-reference chemical data, and the smallest MUE on 87 noncovalent data, with MUEs for these three categories of 4.75, 1.85, and 0.25 kcal mol, respectively, as compared to the average MUEs of the other 82 functionals of 14.0, 4.63, and 1.98 kcal mol. The MUE for 17 absolute atomic energies is 7.4 kcal mol as compared to an average MUE of the other 82 functionals of 34.6 kcal mol. We further tested MN15 for 10 transition-metal coordination energies, the entire S66x8 database of noncovalent interactions, 21 transition-metal reaction barrier heights, 69 electronic excitation energies of organic molecules, 31 semiconductor band gaps, seven transition-metal dimer bond lengths, and 193 bond lengths of 47 organic molecules. The MN15 functional not only performs very well for our training set, which has 481 pieces of data, but also performs very well for our test set, which has 823 data that are not in our training set. The test set includes both ground-state properties and molecular excitation energies. For the latter MN15 achieves simultaneous accuracy for both valence and Rydberg electronic excitations when used with linear-response time-dependent density functional theory, with an MUE of less than 0.3 eV for both types of excitations.
科恩-沈密度泛函被广泛使用;然而,目前没有任何一种可用的交换关联泛函能够以化学精度预测所有化学性质。在此,我们报告一种名为MN15的新泛函,它比以往任何一种泛函都具有更广泛的精度。参数化过程中考虑的性质包括键能、原子化能、电离势、电子亲和能、质子亲和能、反应势垒高度、非共价相互作用、烃类热化学、异构化能、电子激发能、绝对原子能量以及分子结构。与文献中定义的其他82种密度泛函相比,对于54个固有多组态体系的数据,MN15的平均绝对误差(MUE)第二小;对于313个单参考化学数据,其MUE最小;对于87个非共价数据,其MUE也最小,这三类数据的MUE分别为4.75、1.85和0.25 kcal/mol,而其他82种泛函的平均MUE分别为14.0、4.63和1.98 kcal/mol。17个绝对原子能量的MUE为7.4 kcal/mol,而其他82种泛函的平均MUE为34.6 kcal/mol。我们进一步对MN15进行了测试,涉及10种过渡金属配位能、整个非共价相互作用的S66x8数据库、21种过渡金属反应势垒高度、69种有机分子的电子激发能、31种半导体带隙、7种过渡金属二聚体键长以及47种有机分子的193个键长。MN15泛函不仅在我们包含481条数据的训练集上表现出色,而且在我们不包含在训练集中的823条数据的测试集上也表现出色。测试集包括基态性质和分子激发能。对于后者,当与线性响应含时密度泛函理论一起使用时,MN15对价电子激发和里德堡电子激发都能实现同时高精度预测,两种激发类型的MUE均小于0.3 eV。