The Source of Some Empirical Density Functionals van der Waals Forces

被引:0
作者
Leonov, A. V. [1 ,2 ]
Zaripov, D. U. [2 ,3 ]
Dokin, R. Yu. [2 ]
Losev, T. V. [1 ,2 ]
Gerasimov, I. S. [2 ]
Medvedev, M. G. [2 ,3 ]
机构
[1] M V Lomonosov Moscow State Univ, Moscow 119991, Russia
[2] Russian Acad Sci, N D Zelinsky Inst Organ Chem, Moscow 119991, Russia
[3] HSE Univ, Moscow 101000, Russia
关键词
EXCHANGE; MOLECULES; LIBXC;
D O I
10.1021/acs.jpca.4c07586
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Density functional approximations became indispensable tools in many fields of chemistry due to their excellent cost-to-accuracy ratio. Still, consideration is required to select an appropriate approximation for each task. Highly parameterized Minnesota functionals are known for their excellent accuracy in reproducing thermochemical properties and, in particular, weak medium-range interactions. Here, we show that the latter ability of many Minnesota functionals comes from exploiting the basis set incompleteness. This finding shows how empirical functionals can trick their makers by learning to operate in a physics-defying way and likely explains the previously observed tendency of Minnesota functionals to distort electron densities. Thus, satisfaction of the Hellmann-Feynman theorem should be considered an important test and parameterization goal for the future generations of highly parameterized density functionals, including those based on neural networks.
引用
收藏
页码:2806 / 2811
页数:6
相关论文
共 47 条
  • [1] Mardirossian N., Head-Gordon M., Thirty Years of Density Functional Theory in Computational Chemistry: An Overview and Extensive Assessment of 200 Density Functionals, Mol. Phys., 115, pp. 2315-2372, (2017)
  • [2] Peverati R., Truhlar D.G., Quest for a Universal Density Functional: The Accuracy of Density Functionals across a Broad Spectrum of Databases in Chemistry and Physics, Philos. Trans. R. Soc., A, 372, 2011, (2014)
  • [3] Yu H.S., Li S.L., Truhlar D.G., Perspective: Kohn-Sham Density Functional Theory Descending a Staircase, J. Chem. Phys., 145, 13, (2016)
  • [4] Ryabov A., Akhatov I., Zhilyaev P., Neural Network Interpolation of Exchange-Correlation Functional, Sci. Rep., 10, 1, (2020)
  • [5] Nagai R., Akashi R., Sugino O., Completing Density Functional Theory by Machine Learning Hidden Messages from Molecules, npj Comput. Mater., 6, 1, (2020)
  • [6] Kirkpatrick J., McMorrow B., Turban D.H.P., Gaunt A.L., Spencer J.S., Matthews A.G.D.G., Obika A., Thiry L., Fortunato M., Pfau D., Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem, Science, 374, 6573, pp. 1385-1389, (2021)
  • [7] Perdew J.P., Artificial Intelligence “Sees” Split Electrons, Science, 374, 6573, pp. 1322-1323, (2021)
  • [8] Gerasimov I.S., Losev T.V., Epifanov E.Y., Rudenko I., Bushmarinov I.S., Ryabov A.A., Zhilyaev P.A., Medvedev M.G., Comment on “Pushing the Frontiers of Density Functionals by Solving the Fractional Electron Problem, Science, 377, 6606, (2022)
  • [9] Hermann J., DiStasio R.A., Tkatchenko A., First-Principles Models for van Der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications, Chem. Rev., 117, 6, pp. 4714-4758, (2017)
  • [10] Foster M.E., Sohlberg K., Empirically Corrected DFT and Semi-Empirical Methods for Non-Bonding Interactions, Phys. Chem. Chem. Phys., 12, 2, pp. 307-322, (2010)