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Localization in the SCAN meta-generalized gradient approximation functional leading to broken symmetry ground states
Yubo Zhang1, Wenqing Zhang1, David J Singh2
1Department of Physics and Shenzhen Institute for Quantum Science & Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China. zhangwq@sustech.edu.cn and Guangdong Provincial Key Lab for Computational Science and Materials Design, and Shenzhen Municipal Key Lab for Advanced Quantum Materials and Devices, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Abstract:
Density functional theory calculations play a central role in understanding chemical and solid-state systems. Progress depends on density functionals that accurately reproduce both energies, for thermochemistry, and properly describe ground states and other properties that are of interest. The Cr dimer, benzene and graphene are particularly important benchmark systems for quantum chemistry and condensed matter physics. The Strongly Constrained and Appropriately Normed (SCAN) functional, which is an advanced meta-generalized gradient approximation functional that significantly improves molecular energies is shown to perform poorly for the Cr dimer. This is connected with its poor performance for itinerant solid-state magnets and is a consequence of over localization of electrons, thus illustrating an analogy between the Cr dimer and itinerant magnets. The Cr dimer is a notoriously difficult system for density functionals. However, we additionally find that SCAN predicts an incorrect symmetry broken ground state for 2D graphene and for the benzene molecule, which is surprising considering that ground states of these are known to be well described even by the simplest local density approximation. We show that SCAN overly favors localized spin polarized states, which is a serious deficiency of this approach. Thus, the challenge of finding density functionals that accurately treat both localized and delocalized electronic systems remains.
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